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Coronavirus disease 2019 (COVID-19): A literature review

Harapan harapan.

a Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia

b Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia

c Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia

d Division of Infectious Diseases, AichiCancer Center Hospital, Chikusa-ku Nagoya, Japan

Amanda Yufika

e Department of Family Medicine, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia

Wira Winardi

f Department of Pulmonology and Respiratory Medicine, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia

g School of Medicine, The University of Western Australia, Perth, Australia

Haypheng Te

h Siem Reap Provincial Health Department, Ministry of Health, Siem Reap, Cambodia

Dewi Megawati

i Department of Microbiology and Parasitology, Faculty of Medicine and Health Sciences, Warmadewa University, Denpasar, Indonesia

j Department of Medical Microbiology and Immunology, University of California, Davis, CA, USA

Zinatul Hayati

k Department of Clinical Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia

Abram L. Wagner

l Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, MI 48109, USA

Mudatsir Mudatsir

In early December 2019, an outbreak of coronavirus disease 2019 (COVID-19), caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), occurred in Wuhan City, Hubei Province, China. On January 30, 2020 the World Health Organization declared the outbreak as a Public Health Emergency of International Concern. As of February 14, 2020, 49,053 laboratory-confirmed and 1,381 deaths have been reported globally. Perceived risk of acquiring disease has led many governments to institute a variety of control measures. We conducted a literature review of publicly available information to summarize knowledge about the pathogen and the current epidemic. In this literature review, the causative agent, pathogenesis and immune responses, epidemiology, diagnosis, treatment and management of the disease, control and preventions strategies are all reviewed.

On December 31, 2019, the China Health Authority alerted the World Health Organization (WHO) to several cases of pneumonia of unknown aetiology in Wuhan City in Hubei Province in central China. The cases had been reported since December 8, 2019, and many patients worked at or lived around the local Huanan Seafood Wholesale Market although other early cases had no exposure to this market [1] . On January 7, a novel coronavirus, originally abbreviated as 2019-nCoV by WHO, was identified from the throat swab sample of a patient [2] . This pathogen was later renamed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by the Coronavirus Study Group [3] and the disease was named coronavirus disease 2019 (COVID-19) by the WHO. As of January 30, 7736 confirmed and 12,167 suspected cases had been reported in China and 82 confirmed cases had been detected in 18 other countries [4] . In the same day, WHO declared the SARS-CoV-2 outbreak as a Public Health Emergency of International Concern (PHEIC) [4] .

According to the National Health Commission of China, the mortality rate among confirmed cased in China was 2.1% as of February 4 [5] and the mortality rate was 0.2% among cases outside China [6] . Among patients admitted to hospitals, the mortality rate ranged between 11% and 15% [7] , [8] . COVID-19 is moderately infectious with a relatively high mortality rate, but the information available in public reports and published literature is rapidly increasing. The aim of this review is to summarize the current understanding of COVID-19 including causative agent, pathogenesis of the disease, diagnosis and treatment of the cases, as well as control and prevention strategies.

The virus: classification and origin

SARS-CoV-2 is a member of the family Coronaviridae and order Nidovirales. The family consists of two subfamilies, Coronavirinae and Torovirinae and members of the subfamily Coronavirinae are subdivided into four genera: (a) Alphacoronavirus contains the human coronavirus (HCoV)-229E and HCoV-NL63; (b) Betacoronavirus includes HCoV-OC43, Severe Acute Respiratory Syndrome human coronavirus (SARS-HCoV), HCoV-HKU1, and Middle Eastern respiratory syndrome coronavirus (MERS-CoV); (c) Gammacoronavirus includes viruses of whales and birds and; (d) Deltacoronavirus includes viruses isolated from pigs and birds [9] . SARS-CoV-2 belongs to Betacoronavirus together with two highly pathogenic viruses, SARS-CoV and MERS-CoV. SARS-CoV-2 is an enveloped and positive-sense single-stranded RNA (+ssRNA) virus [16] .

SARS-CoV-2 is considered a novel human-infecting Betacoronavirus [10] . Phylogenetic analysis of the SARS-CoV-2 genome indicates that the virus is closely related (with 88% identity) to two bat-derived SARS-like coronaviruses collected in 2018 in eastern China (bat-SL-CoVZC45 and bat-SL-CoVZXC21) and genetically distinct from SARS-CoV (with about 79% similarity) and MERS-CoV [10] . Using the genome sequences of SARS-CoV-2, RaTG13, and SARS-CoV [11] , a further study found that the virus is more related to BatCoV RaTG13, a bat coronavirus that was previously detected in Rhinolophus affinis from Yunnan Province, with 96.2% overall genome sequence identity [11] . A study found that no evidence of recombination events detected in the genome of SARS-CoV-2 from other viruses originating from bats such as BatCoV RaTG13, SARS-CoV and SARSr-CoVs [11] . Altogether, these findings suggest that bats might be the original host of this virus [10] , [11] .

However, a study is needed to elucidate whether any intermediate hosts have facilitated the transmission of the virus to humans. Bats are unlikely to be the animal that is directly responsible for transmission of the virus to humans for several reasons [10] : (1) there were various non-aquatic animals (including mammals) available for purchase in Huanan Seafood Wholesale Market but no bats were sold or found; (2) SARS-CoV-2 and its close relatives, bat-SL-CoVZC45 and bat-SL-CoVZXC21, have a relatively long branch (sequence identity of less than 90%), suggesting those viruses are not direct ancestors of SARS-CoV-2; and (3) in other coronaviruses where bat is the natural reservoir such as SARS-CoV and MERS-CoV, other animals have acted as the intermediate host (civets and possibly camels, respectively). Nevertheless, bats do not always need an intermediary host to transmit viruses to humans. For example, Nipah virus in Bangladesh is transmitted through bats shedding into raw date palm sap [12] .

Transmission

The role of the Huanan Seafood Wholesale Market in propagating disease is unclear. Many initial COVID-19 cases were linked to this market suggesting that SARS-CoV-2 was transmitted from animals to humans [13] . However, a genomic study has provided evidence that the virus was introduced from another, yet unknown location, into the market where it spread more rapidly, although human-to-human transmission may have occurred earlier [14] . Clusters of infected family members and medical workers have confirmed the presence of person-to-person transmission [15] . After January 1, less than 10% of patients had market exposure and more than 70% patients had no exposure to the market [13] . Person-to-person transmission is thought to occur among close contacts mainly via respiratory droplets produced when an infected person coughs or sneezes. Fomites may be a large source of transmission, as SARS-CoV has been found to persist on surfaces up to 96 h [16] and other coronaviruses for up to 9 days [17] .

Whether or not there is asymptomatic transmission of disease is controversial. One initial study published on January 30 reported asymptomatic transmission [18] , but later it was found that the researchers had not directly interviewed the patient, who did in fact have symptoms prior to transmitting disease [19] . A more recent study published on February 21 also purported asymptomatic transmission [20] , but any such study could be limited by errors in self-reported symptoms or contact with other cases and fomites.

Findings about disease characteristics are rapidly changing and subject to selection bias. A study indicated the mean incubation period was 5.2 days (95% confidence interval [95%CI]: 4.1–7.0) [13] . The incubation period has been found to be as long as 19 or 24 days [21] , [22] , although case definitions typically rely on a 14 day window [23] .

The basic reproductive number ( R 0 ) has been estimated with varying results and interpretations. R 0 measures the average number of infections that could result from one infected individual in a fully susceptible population [24] . Studies from previous outbreaks found R 0 to be 2.7 for SARS [25] and 2.4 for 2009 pandemic H1N1 influenza [26] . One study estimated that that basic reproductive number ( R 0 ) was 2.2 (95% CI: 1.4–3.9) [13] . However, later in a further analysis of 12 available studies found that R 0 was 3.28 [27] . Because R 0 represents an average value it is also important to consider the role of super spreaders, who may be hugely responsible for outbreaks within large clusters but who would not largely influence the value of R 0 [28] . During the acute phase of an outbreak or prepandemic, R 0 may be unstable [24] .

In pregnancy, a study of nine pregnancy women who developed COVID-19 in late pregnancy suggested COVID-19 did not lead to substantially worse symptoms than in nonpregnant persons and there is no evidence for intrauterine infection caused by vertical transmission [29] .

In hospital setting, a study involving 138 COVID-19 suggested that hospital-associated transmission of SARS-CoV-2 occurred in 41% of patients [30] . Moreover, another study on 425 patients found that the proportion of cases in health care workers gradually increased by time [13] . These cases likely reflect exposure to a higher concentration of virus from sustained contact in close quarters.

Outside China, as of February 12, 2020, there were 441 confirmed COVID-19 cases reported in 24 countries [6] of which the first imported case was reported in Thailand on January 13, 2020 [6] , [31] . Among those countries, 11 countries have reported local transmission with the highest number of cases reported in Singapore with 47 confirmed cases [6] .

Risk factors

The incidence of SARS-CoV-2 infection is seen most often in adult male patients with the median age of the patients was between 34 and 59 years [20] , [30] , [7] , [32] . SARS-CoV-2 is also more likely to infect people with chronic comorbidities such as cardiovascular and cerebrovascular diseases and diabetes [8] . The highest proportion of severe cases occurs in adults ≥60 years of age, and in those with certain underlying conditions, such as cardiovascular and cerebrovascular diseases and diabetes [20] , [30] . Severe manifestations maybe also associated with coinfections of bacteria and fungi [8] .

Fewer COVID-19 cases have been reported in children less than 15 years [20] , [30] , [7] , [32] . In a study of 425 COVID-19 patients in Wuhan, published on January 29, there were no cases in children under 15 years of age [13] , [33] . Nevertheless, 28 paediatric patients have been reported by January 2020 [34] . The clinical features of infected paediatric patients vary, but most have had mild symptoms with no fever or pneumonia, and have a good prognosis [34] . Another study found that although a child had radiological ground-glass lung opacities, the patient was asymptomatic [35] . In summary, children might be less likely to be infected or, if infected, present milder manifestations than adults; therefore, it is possible that their parents will not seek out treatment leading to underestimates of COVID-19 incidence in this age group.

Pathogenesis and immune response

Like most other members of the coronavirus family, Betacoronavirus exhibit high species specificity, but subtle genetic changes can significantly alter their tissue tropism, host range, and pathogenicity. A striking example of the adaptability of these viruses is the emergence of deadly zoonotic diseases in human history caused by SARS-CoV [36] and MERS-CoV [37] . In both viruses, bats served as the natural reservoir and humans were the terminal host, with the palm civet and dromedary camel the intermediary host for SARS-CoV and MERS-CoV, respectively [38] , [39] . Intermediate hosts clearly play a critical role in cross species transmission as they can facilitate increased contact between a virus and a new host and enable further adaptation necessary for an effective replication in the new host [40] . Because of the pandemic potential of SARS-CoV-2, careful surveillance is immensely important to monitor its future host adaptation, viral evolution, infectivity, transmissibility, and pathogenicity.

The host range of a virus is governed by multiple molecular interactions, including receptor interaction. The envelope spike (S) protein receptor binding domain of SARS-CoV-2 was shown structurally similar to that of SARS-CoV, despite amino acid variation at some key residues [10] . Further extensive structural analysis strongly suggests that SARS-CoV-2 may use host receptor angiotensin-converting enzyme 2 (ACE2) to enter the cells [41] , the same receptor facilitating SARS-CoV to infect the airway epithelium and alveolar type 2 (AT2) pneumocytes, pulmonary cells that synthesize pulmonary surfactant [42] . In general, the spike protein of coronavirus is divided into the S1 and S2 domain, in which S1 is responsible for receptor binding and S2 domain is responsible for cell membrane fusion [10] . The S1 domain of SARS-CoV and SARS-CoV-2 share around 50 conserved amino acids, whereas most of the bat-derived viruses showed more variation [10] . In addition, identification of several key residues (Gln493 and Asn501) that govern the binding of SARS-CoV-2 receptor binding domain with ACE2 further support that SARS-CoV-2 has acquired capacity for person-to-person transmission [41] . Although, the spike protein sequence of receptor binding SARS-CoV-2 is more similar to that of SARS-CoV, at the whole genome level SARS-CoV-2 is more closely related to bat-SL-CoVZC45 and bat-SL-CoVZXC21 [10] .

However, receptor recognition is not the only determinant of species specificity. Immediately after binding to their receptive receptor, SARS-CoV-2 enters host cells where they encounter the innate immune response. In order to productively infect the new host, SARS-CoV-2 must be able to inhibit or evade host innate immune signalling. However, it is largely unknown how SARS-CoV-2 manages to evade immune response and drive pathogenesis. Given that COVID-19 and SARS have similar clinical features [7] , SARS-CoV-2 may have a similar pathogenesis mechanism as SARS-CoV. In response to SARS-CoV infections, the type I interferon (IFN) system induces the expression of IFN-stimulated genes (ISGs) to inhibit viral replication. To overcome this antiviral activity, SARS-CoV encodes at least 8 viral antagonists that modulate induction of IFN and cytokines and evade ISG effector function [43] .

The host immune system response to viral infection by mediating inflammation and cellular antiviral activity is critical to inhibit viral replication and dissemination. However, excessive immune responses together with lytic effects of the virus on host cells will result in pathogenesis. Studies have shown patients suffering from severe pneumonia, with fever and dry cough as common symptoms at onset of illness [7] , [8] . Some patients progressed rapidly with Acute Respiratory Stress Syndrome (ARDS) and septic shock, which was eventually followed by multiple organ failure and about 10% of patients have died [8] . ARDS progression and extensive lung damage in COVID-19 are further indications that ACE2 might be a route of entry for the SARS-CoV-2 as ACE2 is known abundantly present on ciliated cells of the airway epithelium and alveolar type II (cells (pulmonary cells that synthesize pulmonary surfactant) in humans [44] .

Patients with SARS and COVID-19 have similar patterns of inflammatory damage. In serum from patients diagnosed with SARS, there is increased levels of proinflammatory cytokines (e.g. interleukin (IL)-1, IL6, IL12, interferon gamma (IFNγ), IFN-γ-induced protein 10 (IP10), macrophage inflammatory proteins 1A (MIP1A) and monocyte chemoattractant protein-1 (MCP1)), which are associated with pulmonary inflammation and severe lung damage [45] . Likewise, patients infected with SARS-CoV-2 are reported to have higher plasma levels of proinflammatory cytokines including IL1β, IL-2, IL7, TNF-α, GSCF, MCP1 than healthy adults [7] . Importantly, patients in the intensive care unit (ICU) have a significantly higher level of GSCF, IP10, MCP1, and TNF-α than those non-ICU patients, suggesting that a cytokine storm might be an underlying cause of disease severity [7] . Unexpectedly, anti-inflammatory cytokines such as IL10 and IL4 were also increased in those patients [7] , which was uncommon phenomenon for an acute phase viral infection. Another interesting finding, as explained before, was that SARS-CoV-2 has shown to preferentially infect older adult males with rare cases reported in children [7] , [8] . The same trend was observed in primate models of SARS-CoV where the virus was found more likely to infect aged Cynomolgus macaque than young adults [46] . Further studies are necessary to identify the virulence factors and the host genes of SARS-CoV-2 that allows the virus to cross the species-specific barrier and cause lethal disease in humans.

Clinical manifestations

Clinical manifestations of 2019-nCoV infection have similarities with SARS-CoV where the most common symptoms include fever, dry cough, dyspnoea, chest pain, fatigue and myalgia [7] , [30] , [47] . Less common symptoms include headache, dizziness, abdominal pain, diarrhoea, nausea, and vomiting [7] , [30] . Based on the report of the first 425 confirmed cases in Wuhan, the common symptoms include fever, dry cough, myalgia and fatigue with less common are sputum production, headache, haemoptysis, abdominal pain, and diarrhoea [13] . Approximately 75% patients had bilateral pneumonia [8] . Different from SARS-CoV and MERS-CoV infections, however, is that very few COVID-19 patients show prominent upper respiratory tract signs and symptoms such as rhinorrhoea, sneezing, or sore throat, suggesting that the virus might have greater preference for infecting the lower respiratory tract [7] . Pregnant and non-pregnant women have similar characteristics [48] . The common clinical presentation of 2019-nCoV infection are presented in Table 1 .

Clinical symptoms of patients with 2019-nCoV infection.

Severe complications such as hypoxaemia, acute ARDS, arrythmia, shock, acute cardiac injury, and acute kidney injury have been reported among COVID-19 patients [7] , [8] . A study among 99 patients found that approximately 17% patients developed ARDS and, among them, 11% died of multiple organ failure [8] . The median duration from first symptoms to ARDS was 8 days [30] .

Efforts to control spread of COVID-19, institute quarantine and isolation measures, and appropriately clinically manage patients all require useful screening and diagnostic tools. While SARS-CoV-2 is spreading, other respiratory infections may be more common in a local community. The WHO has released a guideline on case surveillance of COVID-19 on January 31, 2020 [23] . For a person who meets certain criteria, WHO recommends to first screen for more common causes of respiratory illness given the season and location. If a negative result is found, the sample should be sent to referral laboratory for SARS-CoV-2 detection.

Case definitions can vary by country and will evolve over time as the epidemiological circumstances change in a given location. In China, a confirmed case from January 15, 2020 required an epidemiological linkage to Wuhan within 2 weeks and clinical features such as fever, pneumonia, and low white blood cell count. On January 18, 2020 the epidemiological criterion was expanded to include contact with anyone who had been in Wuhan in the past 2 weeks [50] . Later, the case definitions removed the epidemiological linkage.

The WHO has put forward case definitions [23] . Suspected cases of COVID-19 are persons (a) with severe acute respiratory infections (history of fever and cough requiring admission to hospital) and with no other aetiology that fully explains the clinical presentation and a history of travel to or residence in China during the 14 days prior to symptom onset; or (b) a patient with any acute respiratory illness and at least one of the following during the 14 days prior to symptom onset: contact with a confirmed or probable case of SARS-CoV-2 infection or worked in or attended a health care facility where patients with confirmed or probable SARS-CoV-2 acute respiratory disease patients were being treated. Probable cases are those for whom testing for SARS-CoV-2 is inconclusive or who test positive using a pan-coronavirus assay and without laboratory evidence of other respiratory pathogens. A confirmed case is one with a laboratory confirmation of SARS-CoV-2 infection, irrespective of clinical signs and symptoms.

For patients who meet diagnostic criteria for SARS-CoV-2 testing, the CDC recommends collection of specimens from the upper respiratory tract (nasopharyngeal and oropharyngeal swab) and, if possible, the lower respiratory tract (sputum, tracheal aspirate, or bronchoalveolar lavage) [51] . In each country, the tests are performed by laboratories designated by the government.

Laboratory findings

Among COVID-19 patients, common laboratory abnormalities include lymphopenia [8] , [20] , [30] , prolonged prothrombin time, and elevated lactate dehydrogenase [30] . ICU-admitted patients had more laboratory abnormalities compared with non-ICU patients [30] , [7] . Some patients had elevated aspartate aminotransferase, creatine kinase, creatinine, and C-reactive protein [20] , [7] , [35] . Most patients have shown normal serum procalcitonin levels [20] , [30] , [7] .

COVID-19 patients have high level of IL1β, IFN-γ, IP10, and MCP1 [7] . ICU-admitted patients tend to have higher concentration of granulocyte-colony stimulating factor (GCSF), IP10, MCP1A, MIP1A, and TNF-α [7] .

Radiology findings

Radiology finding may vary with patients age, disease progression, immunity status, comorbidity, and initial medical intervention [52] . In a study describing 41 of the initial cases of 2019-nCoV infection, all 41 patients had pneumonia with abnormal findings on chest computed tomography (CT-scan) [7] . Abnormalities on chest CT-scan were also seen in another study of 6 cases, in which all of them showed multifocal patchy ground-glass opacities notably nearby the peripheral sections of the lungs [35] . Data from studies indicate that the typical of chest CT-scan findings are bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities [7] , [8] , [20] , [30] , [32] , [53] . The consolidated lung lesions among patients five or more days from disease onset and those 50 years old or older compared to 4 or fewer days and those 50 years or younger, respectively [47] .

As the disease course continue, mild to moderate progression of disease were noted in some cases which manifested by extension and increasing density of lung opacities [49] . Bilateral multiple lobular and subsegmental areas of consolidation are typical findings on chest CT-scan of ICU-admitted patients [7] . A study among 99 patients, one patient had pneumothorax in an imaging examination [8] .

Similar to MERS-CoV and SARS-CoV, there is still no specific antiviral treatment for COVID-19 [54] . Isolation and supportive care including oxygen therapy, fluid management, and antibiotics treatment for secondary bacterial infections is recommended [55] . Some COVID-19 patients progressed rapidly to ARDS and septic shock, which was eventually followed by multiple organ failure [7] , [8] . Therefore, the effort on initial management of COVID-19 must be addressed to the early recognition of the suspect and contain the disease spread by immediate isolation and infection control measures [56] .

Currently, no vaccination is available, but even if one was available, uptake might be suboptimal. A study of intention to vaccinate during the H1N1 pandemic in the United States was around 50% at the start of the pandemic in May 2009 but had decreased to 16% by January 2010 [57] .

Neither is a treatment available. Therefore, the management of the disease has been mostly supportive referring to the disease severity which has been introduced by WHO. If sepsis is identified, empiric antibiotic should be administered based on clinical diagnosis and local epidemiology and susceptibility information. Routine glucocorticoids administration are not recommended to use unless there are another indication [58] . Clinical evidence also does not support corticosteroid treatment [59] . Use of intravenous immunoglobulin might help for severely ill patients [8] .

Drugs are being evaluated in line with past investigations into therapeutic treatments for SARS and MERS [60] . Overall, there is not robust evidence that these antivirals can significantly improve clinical outcomes A. Antiviral drugs such as oseltamivir combined with empirical antibiotic treatment have also been used to treat COVID-19 patients [7] . Remdesivir which was developed for Ebola virus, has been used to treat imported COVID-19 cases in US [61] . A brief report of treatment combination of Lopinavir/Ritonavir, Arbidol, and Shufeng Jiedu Capsule (SFJDC), a traditional Chinese medicine, showed a clinical benefit to three of four COVID-19 patients [62] . There is an ongoing clinical trial evaluating the safety and efficacy of lopinavir-ritonavir and interferon-α 2b in patients with COVID-19 [55] . Ramsedivir, a broad spectrum antivirus has demonstrated in vitro and in vivo efficacy against SARS-CoV-2 and has also initiated its clinical trial [63] , [64] . In addition, other potential drugs from existing antiviral agent have also been proposed [65] , [66] .

Control and prevention strategies

COVID-19 is clearly a serious disease of international concern. By some estimates it has a higher reproductive number than SARS [27] , and more people have been reported to have been infected or died from it than SARS [67] . Similar to SARS-CoV and MERS-CoV, disrupting the chain of transmission is considered key to stopping the spread of disease [68] . Different strategies should be implemented in health care settings and at the local and global levels.

Health care settings can unfortunately be an important source of viral transmission. As shown in the model for SARS, applying triage, following correct infection control measures, isolating the cases and contact tracing are key to limit the further spreading of the virus in clinics and hospitals [68] . Suspected cases presenting at healthcare facilities with symptoms of respiratory infections (e.g. runny nose, fever and cough) must wear a face mask to contain the virus and strictly adhere triage procedure. They should not be permitted to wait with other patients seeking medical care at the facilities. They should be placed in a separated, fully ventilated room and approximately 2 m away from other patients with convenient access to respiratory hygiene supplies [69] . In addition, if a confirmed COVID-19 case require hospitalization, they must be placed in a single patient room with negative air pressure – a minimum of six air changes per hour. Exhausted air has to be filtered through high efficiency particulate air (HEPA) and medical personnel entering the room should wear personal protective equipment (PPE) such as gloves, gown, disposable N95, and eye protection. Once the cases are recovered and discharged, the room should be decontaminated or disinfected and personnel entering the room need to wear PPE particularly facemask, gown, eye protection [69] .

In a community setting, isolating infected people are the primary measure to interrupt the transmission. For example, immediate actions taken by Chinese health authorities included isolating the infected people and quarantining of suspected people and their close contacts [70] . Also, as there are still conflicting assumptions regarding the animal origins of the virus (i.e. some studies linked the virus to bat [71] , [72] while others associated the virus with snake [73] ), contacts with these animal fluids or tissues or consumption of wild caught animal meet should be avoided. Moreover, educating the public to recognize unusual symptoms such as chronic cough or shortness of breath is essential therefore that they could seek medical care for early detection of the virus. If large-scale community transmission occurs, mitigating social gatherings, temporary school closure, home isolation, close monitoring of symptomatic individual, provision of life supports (e.g. oxygen supply, mechanical ventilator), personal hand hygiene, and wearing personal protective equipment such as facemask should also be enforced [74] .

In global setting, locking down Wuhan city was one of the immediate measure taken by Chinese authorities and hence had slowed the global spread of COVID-19 [74] . Air travel should be limited for the cases unless severe medical attentions are required. Setting up temperature check or scanning is mandatory at airport and border to identify the suspected cases. Continued research into the virus is critical to trace the source of the outbreak and provide evidence for future outbreak [74] .

Conclusions

The current COVID-19 pandemic is clearly an international public health problem. There have been rapid advances in what we know about the pathogen, how it infects cells and causes disease, and clinical characteristics of disease. Due to rapid transmission, countries around the world should increase attention into disease surveillance systems and scale up country readiness and response operations including establishing rapid response teams and improving the capacity of the national laboratory system.

Competing interests

The authors declare that they have no competing interests.

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• Published: 16 June 2020

COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research

• Debra L. Weiner 1 , 2 ,
• Vivek Balasubramaniam 3 ,
• Shetal I. Shah 4 &
• Joyce R. Javier 5 , 6

on behalf of the Pediatric Policy Council

Pediatric Research volume  88 ,  pages 148–150 ( 2020 ) Cite this article

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The COVID-19 pandemic has resulted in unprecedented research worldwide. The impact on research in progress at the time of the pandemic, the importance and challenges of real-time pandemic research, and the importance of a pediatrician-scientist workforce are all highlighted by this epic pandemic. As we navigate through and beyond this pandemic, which will have a long-lasting impact on our world, including research and the biomedical research enterprise, it is important to recognize and address opportunities and strategies for, and challenges of research and strengthening the pediatrician-scientist workforce.

The first cases of what is now recognized as SARS-CoV-2 infection, termed COVID-19, were reported in Wuhan, China in December 2019 as cases of fatal pneumonia. By February 26, 2020, COVID-19 had been reported on all continents except Antarctica. As of May 4, 2020, 3.53 million cases and 248,169 deaths have been reported from 210 countries. 1

Impact of COVID-19 on ongoing research

The impact on research in progress prior to COVID-19 was rapid, dramatic, and no doubt will be long term. The pandemic curtailed most academic, industry, and government basic science and clinical research, or redirected research to COVID-19. Most clinical trials, except those testing life-saving therapies, have been paused, and most continuing trials are now closed to new enrollment. Ongoing clinical trials have been modified to enable home administration of treatment and virtual monitoring to minimize participant risk of COVID-19 infection, and to avoid diverting healthcare resources from pandemic response. In addition to short- and long-term patient impact, these research disruptions threaten the careers of physician-scientists, many of whom have had to shift efforts from research to patient care. To protect research in progress, as well as physician-scientist careers and the research workforce, ongoing support is critical. NIH ( https://grants.nih.gov/policy/natural-disasters/corona-virus.htm ), PCORI ( https://www.pcori.org/funding-opportunities/applicant-and-awardee-faqs-related-covid-19 ), and other funders acted swiftly to provide guidance on proposal submission and award management, and implement allowances that enable grant personnel to be paid and time lines to be relaxed. Research institutions have also implemented strategies to mitigate the long-term impact of research disruptions. Support throughout and beyond the pandemic to retain currently well-trained research personnel and research support teams, and to accommodate loss of research assets, including laboratory supplies and study participants, will be required to complete disrupted research and ultimately enable new research.

In the long term, it is likely that the pandemic will force reallocation of research dollars at the expense of research areas funded prior to the pandemic. It will be more important than ever for the pediatric research community to engage in discussion and decisions regarding prioritization of funding goals for dedicated pediatric research and meaningful inclusion of children in studies. The recently released 2020 National Institute of Child Health and Development (NICHD) strategic plan that engaged stakeholders, including scientists and patients, to shape the goals of the Institute, will require modification to best chart a path toward restoring normalcy within pediatric science.

COVID-19 research

This global pandemic once again highlights the importance of research, stable research infrastructure, and funding for public health emergency (PHE)/disaster preparedness, response, and resiliency. The stakes in this worldwide pandemic have never been higher as lives are lost, economies falter, and life has radically changed. Ultimate COVID-19 mitigation and crisis resolution is dependent on high-quality research aligned with top priority societal goals that yields trustworthy data and actionable information. While the highest priority goals are treatment and prevention, biomedical research also provides data critical to manage and restore economic and social welfare.

Scientific and technological knowledge and resources have never been greater and have been leveraged globally to perform COVID-19 research at warp speed. The number of studies related to COVID-19 increases daily, the scope and magnitude of engagement is stunning, and the extent of global collaboration unprecedented. On January 5, 2020, just weeks after the first cases of illness were reported, the genetic sequence, which identified the pathogen as a novel coronavirus, SARS-CoV-2, was released, providing information essential for identifying and developing treatments, vaccines, and diagnostics. As of May 3, 2020 1133 COVID-19 studies, including 148 related to hydroxychloroquine, 13 to remdesivir, 50 to vaccines, and 100 to diagnostic testing, were registered on ClinicalTrials.gov, and 980 different studies on the World Health Organization’s International Clinical Trials Registry Platform (WHO ICTRP), made possible, at least in part, by use of data libraries to inform development of antivirals, immunomodulators, antibody-based biologics, and vaccines. On April 7, 2020, the FDA launched the Coronavirus Treatment Acceleration Program (CTAP) ( https://www.fda.gov/drugs/coronavirus-covid-19-drugs/coronavirus-treatment-acceleration-program-ctap ). On April 17, 2020, NIH announced a partnership with industry to expedite vaccine development ( https://www.nih.gov/news-events/news-releases/nih-launch-public-private-partnership-speed-covid-19-vaccine-treatment-options ). As of May 1, 2020, remdesivir (Gilead), granted FDA emergency use authorization, is the only approved therapeutic for COVID-19. 2

The pandemic has intensified research challenges. In a rush for data already thousands of manuscripts, news reports, and blogs have been published, but to date, there is limited scientifically robust data. Some studies do not meet published clinical trial standards, which now include FDA’s COVID-19-specific standards, 3 , 4 , 5 and/or are published without peer review. Misinformation from studies diverts resources from development and testing of more promising therapeutic candidates and has endangered lives. Ibuprofen, initially reported as unsafe for patients with COVID-19, resulted in a shortage of acetaminophen, endangering individuals for whom ibuprofen is contraindicated. Hydroxychloroquine initially reported as potentially effective for treatment of COVID-19 resulted in shortages for patients with autoimmune diseases. Remdesivir, in rigorous trials, showed decrease in duration of COVID-19, with greater effect given early. 6 Given the limited availability and safety data, the use outside clinical trials is currently approved only for severe disease. Vaccines typically take 10–15 years to develop. As of May 3, 2020, of nearly 100 vaccines in development, 8 are in trial. Several vaccines are projected to have emergency approval within 12–18 months, possibly as early as the end of the year, 7 still an eternity for this pandemic, yet too soon for long-term effectiveness and safety data. Antibody testing, necessary for diagnosis, therapeutics, and vaccine testing, has presented some of the greatest research challenges, including validation, timing, availability and prioritization of testing, interpretation of test results, and appropriate patient and societal actions based on results. 8 Relaxing physical distancing without data regarding test validity, duration, and strength of immunity to different strains of COVID-19 could have catastrophic results. Understanding population differences and disparities, which have been further exposed during this pandemic, is critical for response and long-term pandemic recovery. The “Equitable Data Collection and Disclosure on COVID-19 Act” calls for the CDC (Centers for Disease Control and Prevention) and other HHS (United States Department of Health & Human Services) agencies to publicly release racial and demographic information ( https://bass.house.gov/sites/bass.house.gov/files/Equitable%20Data%20Collection%20and%20Dislosure%20on%20COVID19%20Act_FINAL.pdf )

Trusted sources of up-to-date, easily accessible information must be identified (e.g., WHO https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov , CDC https://www.cdc.gov/coronavirus/2019-nCoV/hcp/index.html , and for children AAP (American Academy of Pediatrics) https://www.aappublications.org/cc/covid-19 ) and should comment on quality of data and provide strategies and crisis standards to guide clinical practice.

Long-term, lessons learned from research during this pandemic could benefit the research enterprise worldwide beyond the pandemic and during other PHE/disasters with strategies for balancing multiple novel approaches and high-quality, time-efficient, cost-effective research. This challenge, at least in part, can be met by appropriate study design, collaboration, patient registries, automated data collection, artificial intelligence, data sharing, and ongoing consideration of appropriate regulatory approval processes. In addition, research to develop and evaluate innovative strategies and technologies to improve access to care, management of health and disease, and quality, safety, and cost effectiveness of care could revolutionize healthcare and healthcare systems. During PHE/disasters, crisis standards for research should be considered along with ongoing and just-in-time PHE/disaster training for researchers willing to share information that could be leveraged at time of crisis. A dedicated funded core workforce of PHE/disaster researchers and funded infrastructure should be considered, potentially as a consortium of networks, that includes physician-scientists, basic scientists, social scientists, mental health providers, global health experts, epidemiologists, public health experts, engineers, information technology experts, economists and educators to strategize, consult, review, monitor, interpret studies, guide appropriate clinical use of data, and inform decisions regarding effective use of resources for PHE/disaster research.

Differences between adult and pediatric COVID-19, the need for pediatric research

As reported by the CDC, from February 12 to April 2, 2020, of 149,760 cases of confirmed COVID-19 in the United States, 2572 (1.7%) were children aged <18 years, similar to published rates in China. 9 Severe illness has been rare. Of 749 children for whom hospitalization data is available, 147 (20%) required hospitalization (5.7% of total children), and 15 of 147 required ICU care (2.0%, 0.58% of total). Of the 95 children aged <1 year, 59 (62%) were hospitalized, and 5 (5.3%) required ICU admission. Among children there were three deaths. Despite children being relatively spared by COVID-19, spread of disease by children, and consequences for their health and pediatric healthcare are potentially profound with immediate and long-term impact on all of society.

We have long been aware of the importance and value of pediatric research on children, and society. COVID-19 is no exception and highlights the imperative need for a pediatrician-scientist workforce. Understanding differences in epidemiology, susceptibility, manifestations, and treatment of COVID-19 in children can provide insights into this pathogen, pathogen–host interactions, pathophysiology, and host response for the entire population. Pediatric clinical registries of COVID-infected, COVID-exposed children can provide data and specimens for immediate and long-term research. Of the 1133 COVID-19 studies on ClinicalTrials.gov, 202 include children aged ≤17 years. Sixty-one of the 681 interventional trials include children. With less diagnostic testing and less pediatric research, we not only endanger children, but also adults by not identifying infected children and limiting spread by children.

Pediatric considerations and challenges related to treatment and vaccine research for COVID-19 include appropriate dosing, pediatric formulation, and pediatric specific short- and long-term effectiveness and safety. Typically, initial clinical trials exclude children until safety has been established in adults. But with time of the essence, deferring pediatric research risks the health of children, particularly those with special needs. Considerations specific to pregnant women, fetuses, and neonates must also be addressed. Childhood mental health in this demographic, already struggling with a mental health pandemic prior to COVID-19, is now further challenged by social disruption, food and housing insecurity, loss of loved ones, isolation from friends and family, and exposure to an infodemic of pandemic-related information. Interestingly, at present mental health visits along with all visits to pediatric emergency departments across the United States are dramatically decreased. Understanding factors that mitigate and worsen psychiatric symptoms should be a focus of research, and ideally will result in strategies for prevention and management in the long term, including beyond this pandemic. Social well-being of children must also be studied. Experts note that the pandemic is a perfect storm for child maltreatment given that vulnerable families are now socially isolated, facing unemployment, and stressed, and that children are not under the watch of mandated reporters in schools, daycare, and primary care. 10 Many states have observed a decrease in child abuse reports and an increase in severity of emergency department abuse cases. In the short term and long term, it will be important to study the impact of access to care, missed care, and disrupted education during COVID-19 on physical and cognitive development.

Training and supporting pediatrician-scientists, such as through NIH physician-scientist research training and career development programs ( https://researchtraining.nih.gov/infographics/physician-scientist ) at all stages of career, as well as fostering research for fellows, residents, and medical students willing to dedicate their research career to, or at least understand implications of their research for, PHE/disasters is important for having an ongoing, as well as a just-in-time surge pediatric-focused PHE/disaster workforce. In addition to including pediatric experts in collaborations and consortiums with broader population focus, consideration should be given to pediatric-focused multi-institutional, academic, industry, and/or government consortiums with infrastructure and ongoing funding for virtual training programs, research teams, and multidisciplinary oversight.

The impact of the COVID-19 pandemic on research and research in response to the pandemic once again highlights the importance of research, challenges of research particularly during PHE/disasters, and opportunities and resources for making research more efficient and cost effective. New paradigms and models for research will hopefully emerge from this pandemic. The importance of building sustained PHE/disaster research infrastructure and a research workforce that includes training and funding for pediatrician-scientists and integrates the pediatrician research workforce into high-quality research across demographics, supports the pediatrician-scientist workforce and pipeline, and benefits society.

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Department of Pediatrics, Division of Emergency Medicine, Boston Children’s Hospital, Boston, MA, USA

Debra L. Weiner

Harvard Medical School, Boston, MA, USA

Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

Vivek Balasubramaniam

Department of Pediatrics and Division of Neonatology, Maria Fareri Children’s Hospital at Westchester Medical Center, New York Medical College, Valhalla, NY, USA

Shetal I. Shah

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Joyce R. Javier

Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

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All authors made substantial contributions to conception and design, data acquisition and interpretation, drafting the manuscript, and providing critical revisions. All authors approve this final version of the manuscript.

Pediatric Policy Council

Scott C. Denne, MD, Chair, Pediatric Policy Council; Mona Patel, MD, Representative to the PPC from the Academic Pediatric Association; Jean L. Raphael, MD, MPH, Representative to the PPC from the Academic Pediatric Association; Jonathan Davis, MD, Representative to the PPC from the American Pediatric Society; DeWayne Pursley, MD, MPH, Representative to the PPC from the American Pediatric Society; Tina Cheng, MD, MPH, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Michael Artman, MD, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Shetal Shah, MD, Representative to the PPC from the Society for Pediatric Research; Joyce Javier, MD, MPH, MS, Representative to the PPC from the Society for Pediatric Research.

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Weiner, D.L., Balasubramaniam, V., Shah, S.I. et al. COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research. Pediatr Res 88 , 148–150 (2020). https://doi.org/10.1038/s41390-020-1006-3

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COVID-19 Research Articles Downloadable Database

March 19, 2020

Updated January 12, 2024

COVID-19 Research Guide Home

• Research Articles Downloadable Database
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Important announcement:  

The CDC Database of COVID-19 Research Articles became a collaboration with the WHO to create the  WHO COVID-19 database  during the pandemic to make it easier for results to be searched, downloaded, and used by researchers worldwide.

The last version of the CDC COVID-19 database was archived and remain available on this website.  Please note that it has stopped updating as of October 9, 2020 and all new articles were integrated into the  WHO COVID-19 database .  The WHO Covid-19 Research Database was a resource created in response to the Public Health Emergency of International Concern (PHEIC). Its content remains searchable and spans the time period March 2020 to June 2023. Since June 2023, manual updates to the database have been discontinued.

If you have any questions, concerns, or problems accessing the WHO COVID-19 Database please email the CDC Library for assistance.

Materials listed in these guides are selected to provide awareness of quality public health literature and resources. A material’s inclusion does not necessarily represent the views of the U.S. Department of Health and Human Services (HHS), the Public Health Service (PHS), or the Centers for Disease Control and Prevention (CDC), nor does it imply endorsement of the material’s methods or findings.

Below are options to download the archive of COVID-19 research articles.  You can search the database of citations by author, keyword (in title, author, abstract, subject headings fields), journal, or abstract when available.  DOI, PMID, and URL links are included when available.

This database was last updated on October 9, 2020 .

• The CDC Database of COVID-19 Research Articles is now a part of the WHO COVID-19 database .  Our new  search results are now being sent to the WHO COVID-19 Database to make it easier for them to be searched, downloaded, and used by researchers worldwide. The WHO Covid-19 Research Database was a resource created in response to the Public Health Emergency of International Concern (PHEIC). Its content remains searchable and spans the time period March 2020 to June 2023. Since June 2023, manual updates to the database have been discontinued.
• To help inform CDC’s COVID-19 Response, as well as to help CDC staff stay up to date on the latest COVID-19 research, the Response’s Office of the Chief Medical Officer has collaborated with the CDC Office of Library Science to create a series called COVID-19 Science Update . This series, the first of its kind for a CDC emergency response, provides brief summaries of new COVID-19-related studies on many topics, including epidemiology, clinical treatment and management, laboratory science, and modeling. As of December 18, 2021, CDC has stopped production of the weekly COVID-19 Science Update.

Excel download:

• Articles from August until October 9 2020 [XLS – 29 MB]
• Articles from December 2019 through July 2020 [XLS – 45 MB]
• The CDC Database of COVID-19 Research Articles is now a part of the WHO COVID-19 database .  Our new search results are now being sent to the WHO COVID-19 Database to make it easier for them to be searched, downloaded, and used by researchers worldwide.
• October 8 in Excel [XLS – 1 MB]
• October 7 in Excel [XLS – 1 MB]
• October 6 in Excel [XLS – 1 MB]
• Note the main Excel file can also be sorted by date added.

Citation Management Software (EndNote, Mendeley, Zotero, Refman, etc.)  download:

• Part 1 [ZIP – 38 MB]
• Part 2 [ZIP – 43 MB]
• October 8 in citation management software format [RIS – 2 MB]
• October 7 in citation management software format [RIS – 2 MB]
• October 6 in citation management software format [RIS – 2 MB]
• Note the main RIS file can also be sorted by date added.

The COVID-19 pandemic is a rapidly changing situation.  Some of the research included above is preliminary.  Materials listed in this database are selected to provide awareness of quality public health literature and resources. A material’s inclusion does not necessarily represent the views of the U.S. Department of Health and Human Services (HHS), the Public Health Service (PHS), or the Centers for Disease Control and Prevention (CDC), nor does it imply endorsement of the material’s methods or findings.

To access the full text, click on the DOI, PMID, or URL links.  While most publishers are making their COVID-19 content Open Access, some articles are accessible only to those with a CDC user id and password. Find a library near you that may be able to help you get access to articles by clicking the following links: https://www.worldcat.org/libraries OR https://www.usa.gov/libraries .

CDC users can use EndNote’s Find Full Text feature to attach the full text PDFs within their EndNote Library.  CDC users, please email Martha Knuth for an EndNote file of all citations.  Once you have your EndNote file downloaded, to get the full-text of journal articles listed in the search results you can do the following steps:

• First, try using EndNote’s “Find Full-Text” feature to attach full-text articles to your EndNote Library.
• Next, check for full-text availability, via the E-Journals list, at: http://sfxhosted.exlibrisgroup.com/cdc/az   .
• If you can’t find full-text online, you can request articles via DocExpress, at: https://docexpress.cdc.gov/illiad/

The following databases were searched from Dec. 2019-Oct. 9 2020 for articles related to COVID-19: Medline (Ovid and PubMed), PubMed Central, Embase, CAB Abstracts, Global Health, PsycInfo, Cochrane Library, Scopus, Academic Search Complete, Africa Wide Information, CINAHL, ProQuest Central, SciFinder, the Virtual Health Library, and LitCovid.  Selected grey literature sources were searched as well, including the WHO COVID-19 website, CDC COVID-19 website, Eurosurveillance, China CDC Weekly, Homeland Security Digital Library, ClinicalTrials.gov, bioRxiv (preprints), medRxiv (preprints), chemRxiv (preprints), and SSRN (preprints).

Detailed search strings with synonyms used for COVID-19 are below.

Detailed search strategy for gathering COVID-19 articles, updated October 9, 2020 [PDF – 135 KB]

Note on preprints:   Preprints have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information.

Materials listed in these guides are selected to provide awareness of quality public health literature and resources. A material’s inclusion does not necessarily represent the views of the U.S. Department of Health and Human Services (HHS), the Public Health Service (PHS), or the Centers for Disease Control and Prevention (CDC), nor does it imply endorsement of the material’s methods or findings. HHS, PHS, and CDC assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by HHS, PHS, and CDC. Opinion, findings, and conclusions expressed by the original authors of items included in these materials, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of HHS, PHS, or CDC. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by HHS, PHS, or CDC.

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Covid-19 data analysis and computation with urban structure consideration  †.

1. Introduction

2. data collections, 2.1. infected individuals, hospitalizations, and bed capacities, 2.2. vaccination rate, 2.3. average temperature, 2.4. mobility changes, 3. data analysis, 4. model development.

• Feature Selection: relevant features were selected based on the correlation and time series analysis conducted in the previous session;
• Data Preparation: the data were split into training and testing sets to validate the model’s performance;
• Model Training: the model was trained on the training data;
• Model Evaluation: the model was evaluated using appropriate metrics such as R-squared and Mean Squared Error;

4.1. Linear Regression Model

4.2. random forest model, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Ministry of Land, Infrastructure, Transport and Tourism. Compact Town Development Based on Public Transportation. Available online: https://www.mlit.go.jp/common/001095208.pdf (accessed on 17 March 2024).
• “Indicators for Determining COVID-19 Level” such as Hospital Bed Usage Rate by Prefecture. Available online: https://www3.nhk.or.jp/news/special/coronavirus/level/ (accessed on 17 March 2024).
• First State of Emergency Declaration. Available online: https://www3.nhk.or.jp/news/special/coronavirus/emergency/ (accessed on 17 March 2024).
• What Are the Priority Measures to Prevent the Spread? Available online: https://www.kaonavi.jp/dictionary/manenboshitojyutensochi/ (accessed on 17 March 2024).
• Ministry of Health, Labor and Welfare. New Coronavirus Infection Situation from Data. Available online: https://covid19.mhlw.go.jp/ (accessed on 17 March 2024).
• Ministry of Health, Labor and Welfare. Survey on Medical Treatment Status, Number of Inpatient Beds, etc. Available online: https://www.mhlw.go.jp/stf/seisakunitsuite/newpage_00023.html (accessed on 17 March 2024).
• Ministry of Health, Labor and Welfare. Press Release Materials Regarding the New Coronavirus Infection. Available online: https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000121431_00438.html (accessed on 17 March 2024).
• Digital Agency. New Coronavirus Vaccination Status. Available online: https://info.vrs.digital.go.jp/dashboard (accessed on 17 March 2024).
• Japan Meteorological Agency. Historical Weather Data Resource. Available online: https://www.data.jma.go.jp/risk/obsdl/ (accessed on 17 March 2024).
• Google. Community Mobility Reports. Available online: https://www.google.com/covid19/mobility// (accessed on 17 March 2024).

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• Article Information

County-level death counts for Florida and Ohio based on Datavant data that link mortality records to voter registration files against the CDC data. Each observation represents a single county. Additional details on the data are provided in the eMethods in Supplement 1 .

Weekly excess deaths for Florida and Ohio based on mortality records linked to voter registration files. A, Overall excess death rates in Florida and Ohio. B, Excess death rates by registered party. C, The percentage-point difference between the registered parties, after adjusting for age and state-level differences; the smooth brown curve was fit with locally estimated scatterplot smoothing. A-C, Excess death rates were calculated for each week by comparing the observed deaths in that week with expected deaths based on a Poisson model. The 95% prediction intervals (shaded areas) were determined using simulations from the Poisson coefficient and outcome distribution, with SEs clustered at the county level. Additional details on the excess death methods and statistical analyses are presented in the eMethods in Supplement 1 .

The analyses were additionally adjusted for age and state-level differences in subgroup analyses where these covariates were not used for stratification. The 95% prediction intervals (horizontal lines) were determined using simulations from the Poisson coefficient and outcome distribution, with SEs clustered at the county level. Additional details on the excess death methodology and statistical analyses are presented in the eMethods in Supplement 1 .

The diamonds are binned means; counties with similar vaccination rates were binned to form 8 equally sized bins. The curves were fit to the underlying data using locally estimated scatterplot smoothing. In the pre–COVID-19 period (before April 2020), excess death rates for both Republican and Democratic voters hover around 0. During the beginning pandemic but before open vaccine eligibility (April 2020 to March 2021), the association between excess death rates and county-level vaccination rates were generally negative and nearly identical for Republican and Democratic voters. However, in the period after open vaccine eligibility (April 2021 to December 2021), there was a clear difference between Republican and Democratic voters, with higher excess death rates for Republicans concentrated in counties with lower overall vaccination rates and minimal differences in counties with the highest vaccination rates.

eMethods . Supplemental Description of Methods

eFigure 1. Excess Death Rates by Age in Florida and Ohio: 2018-2021

eFigure 2. Excess Death Rates in Florida: 2018-2021

eFigure 3. Excess Death Rates in Ohio: 2018-2021

eFigure 4. Excess Death Rates and Vaccination Rates in Florida and Ohio During the COVID-19 Pandemic Using October 1, 2021, Vaccination Rates

eFigure 5. Excess Death Rates and Vaccination Rates in Florida and Ohio During the COVID-19 Pandemic Using March 1, 2021, Vaccination Rates

eTable 1. Summary Statistics

eTable 2. Sensitivity of Estimated Difference in Excess Death Rates Between Republican and Democratic Voters to Alterations in Excess Death Methodology and Statistical Model

Data Sharing Statement

• Discrepancies in Estimating Excess Death by Political Party Affiliation—Reply JAMA Internal Medicine Comment & Response January 1, 2024 Jacob Wallace, PhD; Paul Goldsmith-Pinkham, PhD; Jason L. Schwartz, PhD
• Discrepancies in Estimating Excess Death by Political Party Affiliation JAMA Internal Medicine Comment & Response January 1, 2024 Christopher Dasaro, BS; Alyson Haslam, PhD; Vinay Prasad, MD, MPH
• Discrepancies in Estimating Excess Death by Political Party Affiliation JAMA Internal Medicine Comment & Response January 1, 2024 Patrick O’Mahen, PhD

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For this paper to be accurate, voting records must be accurate. Ohio has historically had inaccurate voting records, so much so that a recent Supreme Court Decision recently went against the state of Ohio, see https://www.judicialwatch.org/wp-content/uploads/2018/06/Jon-Husted-Ohio-Secretary-of-State-v.-Philip-Randolph-Institute-et-al.-decision-16-980.pdf.

Also this paper contradicts more recent studies that have showed that mRNA vaccination decreases death rates from COVID, but increases deaths from other causes, so that all cause mortality is unchanged, with a relative risk of dying of 1.03 in the vaccinated group vas the unvaccinated group. https://www.cell.com/iscience/fulltext/S2589-0042(23)00810-6

This is a very interesting and informative study of public health value, but the findings are not unexpected.

It demonstrates the value of preventing disruption of health, and perhaps equally importantly emphasizes the direct and indirect economic loss to the society and the state when public health is compromised.

Political affiliation should not influence health care. Medicine is beyond politics!

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Wallace J , Goldsmith-Pinkham P , Schwartz JL. Excess Death Rates for Republican and Democratic Registered Voters in Florida and Ohio During the COVID-19 Pandemic. JAMA Intern Med. 2023;183(9):916–923. doi:10.1001/jamainternmed.2023.1154

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Excess Death Rates for Republican and Democratic Registered Voters in Florida and Ohio During the COVID-19 Pandemic

• 1 Yale School of Public Health, New Haven, Connecticut
• 2 Yale School of Management, New Haven, Connecticut
• Comment & Response Discrepancies in Estimating Excess Death by Political Party Affiliation—Reply Jacob Wallace, PhD; Paul Goldsmith-Pinkham, PhD; Jason L. Schwartz, PhD JAMA Internal Medicine
• Comment & Response Discrepancies in Estimating Excess Death by Political Party Affiliation Christopher Dasaro, BS; Alyson Haslam, PhD; Vinay Prasad, MD, MPH JAMA Internal Medicine
• Comment & Response Discrepancies in Estimating Excess Death by Political Party Affiliation Patrick O’Mahen, PhD JAMA Internal Medicine

Question   Was political party affiliation a risk factor associated with excess mortality during the COVID-19 pandemic in Florida and Ohio?

Findings   In this cohort study evaluating 538 159 deaths in individuals aged 25 years and older in Florida and Ohio between March 2020 and December 2021, excess mortality was significantly higher for Republican voters than Democratic voters after COVID-19 vaccines were available to all adults, but not before. These differences were concentrated in counties with lower vaccination rates, and primarily noted in voters residing in Ohio.

Meaning   The differences in excess mortality by political party affiliation after COVID-19 vaccines were available to all adults suggest that differences in vaccination attitudes and reported uptake between Republican and Democratic voters may have been a factor in the severity and trajectory of the pandemic in the US.

Importance   There is evidence that Republican-leaning counties have had higher COVID-19 death rates than Democratic-leaning counties and similar evidence of an association between political party affiliation and attitudes regarding COVID-19 vaccination; further data on these rates may be useful.

Objective   To assess political party affiliation and mortality rates for individuals during the initial 22 months of the COVID-19 pandemic.

Design, Setting, and Participants   A cross-sectional comparison of excess mortality between registered Republican and Democratic voters between March 2020 and December 2021 adjusted for age and state of voter registration was conducted. Voter and mortality data from Florida and Ohio in 2017 linked to mortality records for January 1, 2018, to December 31, 2021, were used in data analysis.

Exposures   Political party affiliation.

Main Outcomes and Measures   Excess weekly deaths during the COVID-19 pandemic adjusted for age, county, party affiliation, and seasonality.

Results   Between January 1, 2018, and December 31, 2021, there were 538 159 individuals in Ohio and Florida who died at age 25 years or older in the study sample. The median age at death was 78 years (IQR, 71-89 years). Overall, the excess death rate for Republican voters was 2.8 percentage points, or 15%, higher than the excess death rate for Democratic voters (95% prediction interval [PI], 1.6-3.7 percentage points). After May 1, 2021, when vaccines were available to all adults, the excess death rate gap between Republican and Democratic voters widened from −0.9 percentage point (95% PI, −2.5 to 0.3 percentage points) to 7.7 percentage points (95% PI, 6.0-9.3 percentage points) in the adjusted analysis; the excess death rate among Republican voters was 43% higher than the excess death rate among Democratic voters. The gap in excess death rates between Republican and Democratic voters was larger in counties with lower vaccination rates and was primarily noted in voters residing in Ohio.

Conclusions and Relevance   In this cross-sectional study, an association was observed between political party affiliation and excess deaths in Ohio and Florida after COVID-19 vaccines were available to all adults. These findings suggest that differences in vaccination attitudes and reported uptake between Republican and Democratic voters may have been factors in the severity and trajectory of the pandemic in the US.

As of May 2023, there had been approximately 1.1 million deaths from COVID-19 in the US. 1 There is evidence that Republican-leaning counties have had higher COVID-19 death rates than Democratic-leaning counties and similar evidence of an association between political party affiliation and attitudes regarding COVID-19 vaccination, social distancing, and other mitigation strategies based on political party affiliation. 2 - 6

Prior studies 7 , 8 have found that Republican-leaning counties have had higher COVID-19 death rates than Democratic-leaning counties. It is unknown whether this county-level association persists at the individual level and whether it may be subject to the ecologic fallacy. 9 The ecologic fallacy is the incorrect assumption that associations observed at an aggregated level (eg, a county) will be the same at the individual level. Republican-leaning and Democratic-leaning counties differ in ways other than political party affiliation, 10 , 11 such as racial and ethnic composition, rurality, and educational levels, making it difficult to establish whether the differences in COVID-19 death rates are associated with political party affiliation or other differences in county-level characteristics. Research before the COVID-19 pandemic has also found evidence of higher death rates in Republican-leaning counties than Democratic-leaning counties. 12

To assess the association between political party affiliation and excess mortality for individuals during the COVID-19 pandemic, we linked voter registration data in Florida and Ohio to mortality data at the individual level to calculate excess death rates for Republican and Democratic voters and compare excess death rates before and after vaccines became available to the full adult population. 13 , 14 Because individual-level vaccination status was not included in the available data, we were able to assess excess death rates and vaccination rates only at the county level.

The eMethods in Supplement 1 provides additional details of all the methods. We obtained detailed US weekly mortality data from January 1, 2018, to December 31, 2021, from Datavant, an organization that augments the Social Security Administration Death Master File with information from newspapers, funeral homes, and other sources to construct an individual-level database containing 10 325 730 deaths in the US to individuals aged 25 or older during this period. This data set, which includes deaths reported to Datavant through March 31, 2023, covers approximately 83.5% of the Centers for Disease Control and Prevention death count for individuals who died at age 25 or older during the period from January 1, 2018, to December 31, 2021. Because the Datavant mortality data do not contain state identifiers, we are unable to assess data completeness in our individual study states of Florida and Ohio. During the COVID-19 pandemic, Datavant mortality data have been used in other peer-reviewed 15 and publicly available 16 research on excess mortality. The Yale University Institutional Review Board exempted the study from review because the data were deidentified, and reporting adheres to the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

We linked the mortality data at the individual level to 2017 Florida and Ohio voter registration files; these were the only states for which historical publicly available voter registration data were readily available. The linkage was performed from April 11 to 14, 2023. For each record, the linked data included week of death, age of deceased, county of residence, and 2017 political party affiliation. Political party affiliation in Ohio was defined by whether an individual voted in a party’s primary election within the preceding 2 calendar years; in Florida, political party affiliation was based on party registration. We excluded individuals whose political party affiliation was independent and those who were affiliated with third parties. Because COVID-19 deaths are most common at older ages, 17 we included only death records for individuals who died at age 25 years or older.

We also obtained death counts for the study period from the National Center for Health Statistics 18 and county-level vaccination rates from the Centers for Disease Control and Prevention. 19 We selected May 1, 2021, as the date for the county-level vaccination rate—1 month after eligibility for vaccines opened to all adults in the study states—because it represented the approximate date when all adults would have had the opportunity to receive at least 1 dose of a COVID-19 vaccine if they so desired, taking into account the time that states required during April 2021 to schedule and administer vaccines to newly eligible adults seeking them. As a robustness check, we assessed the sensitivity of our findings to using county-level vaccination rates on alternative dates before (March 1, 2021) and after (October 1, 2021) May 1, 2021.

We aggregated weekly death counts from January 1, 2018, to December 31, 2021, at the county-by-party-by-age level. The age ranges used were 25 to 64, 65 to 74, 75 to 84, and 85 years or older. The observed death counts included all the deaths from our mortality data that linked to Republican or Democratic voters who were registered in Florida and Ohio as of 2017.

To calculate the number of excess deaths, we estimated the number of deaths we would expect in the absence of the COVID-19 pandemic. First, we estimated expected weekly deaths at the county-by-party-by-age level by fitting a Poisson regression model to observed weekly death counts at the county-by-party-by-age-level for January 1, 2018, through December 31, 2019. 20 , 21 We then predicted expected deaths over our full sample. Excess deaths were defined as the difference between observed and expected deaths for January 1, 2018, to December 31, 2021. As a check on the model, we used predictions from the model in the weeks before the onset of COVID-19 (January 1, 2018, to March 31, 2020) to estimate excess deaths during this period.

We calculated excess death rates (the primary outcome) as the ratio of observed deaths (the numerator) to expected deaths (the denominator). To obtain estimates of excess death rates at aggregated levels, we used a weighted average of estimated excess death rates in each of the underlying cells (eg, county-by-party-by-age), weighted by their expected death counts. We estimated Poisson 95% prediction intervals (PIs), simulating from the coefficient distribution and outcome distribution, with SEs clustered by county. 22 We additionally adjusted estimated differences in excess death rates between Republican and Democratic voters—the primary estimate of interest—for differences in excess death rates by age group and state during the COVID-19 pandemic. Intuitively, this approach compared excess death rates between Democratic and Republican voters of the same age residing in the same states during the same week of the pandemic and then weighted those differences in excess death rates to either the weekly level, when plotting weekly differences in excess death rates, or to 3 broader time periods: (1) April 1, 2020, to December 31, 2021 (the part of the study period overlapping the COVID-19 pandemic); (2) April 1, 2020, to March 31, 2021 (the period during the pandemic before open vaccine eligibility for all adults); and (3) April 1, 2021, to December 31, 2021 (the period during the pandemic after open vaccine eligibility for all adults).

We also assessed county-level vaccination rates (as of May 1, 2021) and excess death rates by plotting average excess death rates for Republican and Democratic voters against the county-level vaccination rate during (1) the pre–COVID-19 pandemic period, (2) the period during the pandemic before open vaccine eligibility for all adults, and (3) the period during the pandemic after open vaccine eligibility for all adults.

In sensitivity analyses, we altered the Poisson model used to predict baseline death counts by including a linear time trend (and in one analysis allowing it to vary by state) and additional seasonality terms to capture higher frequency season-of-the-year trends. 23 For transparency, we calculated differences in the excess death rates between Republican and Democratic voters with no adjustments (removing our state and age group adjustments) and, separately, with a model that included our primary adjustments (state and age group) and additional adjustments for county-by-age differences in excess death rates during the pandemic.

We performed all calculations using R, version 4.1.3 (R Foundation for Statistical Computing). Statistical analyses report 95% PIs using simulations from the coefficient distribution and outcome distribution, with SEs clustered by county. Significance testing was 2-sided, and a P  < .05 was considered statistically significant.

Our study included 538 159 deaths for individuals aged 25 years and older in Florida and Ohio between January 2018 and December 2021 linked to their 2017 voter data (eTable 1 in Supplement 1 ). The median age at death was 78 years (IQR, 71-89 years). The pattern of death counts in our linked data and in the National Center for Health Statistics data was similar ( Figure 1 ).

Using these data, we found a 20.5 percentage-point (95% PI, 15.6-25.6 percentage points) increase in weekly death counts in Florida and Ohio in the March 2020 to December 2021 period relative to the expected death counts for those weeks ( Figure 2 A and Table ). By comparison, for the time periods before the pandemic, we found only small fluctuations in excess death rates around 0.

Before the pandemic, excess death rates for Republican and Democratic voters were centered around 0 ( Figure 2 B). In the winter of 2021, both groups experienced sharp increases of similar magnitude in excess death rates. However, in the summer of 2021, after vaccines were available to all adults, the excess death rate among Republican voters began to increase relative to the excess death rate among Democratic voters; in the fall of 2021, the gap widened further. Between March 2020 and December 2021, excess death rates were 2.8 percentage points (15%) higher for Republican voters compared with Democratic voters (95% PI, 1.6-3.7 percentage points) ( Table ). After April 1, 2021, when all adults were eligible for vaccines in Florida and Ohio, this gap widened from −0.9 percentage point (95% PI, −2.5 to 0.3 percentage points) between March 2020 and March 2021, to 7.7 percentage points (95% PI, 6.0-9.3 percentage points) in the adjusted analysis, or a 43% difference ( Table ).

The estimates of differences in excess death rates between Republican and Democratic voters (adjusted for age, time, and state) were small until the summer of 2021, when excess death rates among Republican voters began to increase compared with excess death rates among Democratic voters ( Figure 2 C). The analyses stratified by age showed that Republican voters had significantly higher excess death rates compared with Democratic voters for 2 of the 4 age groups in the study, the differences for the age group 25 to 64 years were not significant ( Figure 3 ; eFigure 1 in Supplement 1 ). Democratic voters had significantly higher excess death rates compared with Republican voters for the age group 65 to 74 years. The analyses stratified by state showed that differences in excess death rates between Republican and Democratic voters were primarily seen in voters residing in Ohio, with smaller, and generally nonsignificant, differences in weekly excess death rates between Republican and Democratic voters in Florida (eFigure 2 and eFigure 3 in Supplement 1 ). In analyses that pooled data from March 2020 to December 2021, Republican voters in Florida did not have a statistically significantly higher excess death rate than Democratic voters in Florida ( Figure 3 ). Additional sensitivity analyses supported our main conclusions (eTable 2 in Supplement 1 ).

Before the COVID-19 pandemic, there was no association between county-level excess death rates, which hovered around 0, and the county-level vaccination rates ( Figure 4 A). During the pandemic, there was generally a negative association between county-level excess death rates and the share of the county population administered at least 1 dose of the vaccine as of May 1, 2021 ( Figure 4 B and C). In the period before open vaccine eligibility for adults (April 2020 to March 2021), the association between excess death rates and county-level vaccination rates was nearly identical for Republican and Democratic voters ( Figure 4 B). In the period after open vaccine eligibility (April to December 2021), there was a clear difference between Republican and Democratic voters, with higher excess death rates for Republicans in counties with lower overall vaccination rates ( Figure 4 C). Sensitivity analyses supported our main conclusions (eFigure 4 and eFigure 5 in Supplement 1 ).

During the initial years of the COVID-19 pandemic, political party affiliation in the US was associated with excess death rates in Florida and Ohio at the individual level. Republican voters had higher excess death rates than Democratic voters, as noted in a large mortality gap in the period after, but not before, all adults were eligible for vaccines in Florida and Ohio. With adjustments for differences in age and state of residence between Republican and Democratic voters, our findings suggest that, among individuals in the same age groups living in the same states, there were significant differences in excess death rates during the COVID-19 pandemic associated with political party affiliation. The results were robust to alterations in the methods used to estimate excess mortality as well as the statistical model used to estimate the difference in excess death rates between Republican and Democratic voters.

Our findings suggest that political party affiliation became a substantial factor only after COVID-19 vaccines were available to all adults in the US. Although the lack of individual-level vaccination status limited our ability to note further associations, the results suggest that well-documented differences in vaccination attitudes and reported uptake between Republican and Democratic voters 24 , 25 may have been factors in the severity and trajectory of the pandemic. However, one alternative explanation is that political party affiliation is a proxy for other risk factors (beyond age, which we adjusted for) for excess mortality during the COVID-19 pandemic, such as rates of underlying medical conditions, race and ethnicity, socioeconomic status, or health insurance coverage, 26 - 29 and these risk factors may be associated with differences in excess mortality by political party, even though we only observed differences in excess mortality after vaccines were available to all adults. It is also possible that specific risk factors for excess mortality interact with the emergence of COVID-19 variants (eg, Delta) or changes in vaccine-associated protection over time to be more consequential at different stages of the pandemic. Because data limitations prevented us from directly adjusting for these factors, their potential influence remains an important question for future research.

In addition to vaccines, nonpharmaceutical interventions, including facial masks and restrictions on large gatherings, have been reported to contribute to reductions in transmission of COVID-19 or its severe outcomes, including death, in experimental, quasi-experimental, and modeling studies. 30 - 33 However, differences in support for these measures by political party affiliation emerged early in the pandemic, 34 and the gradual loosening of the strictest government policies regarding the use of facial masks and restrictions on large gatherings predated April 2021, when vaccines became available to all adults in the study states. The extent of public adherence to these and other interventions at various stages of the pandemic, associations between individual political party affiliation and the adoption over time of these interventions in specific geographic areas, and their relative contribution to trends in individual and community COVID-19 mortality over time are also worthwhile areas for further investigation.

Since the fall of 2022, the focus of the US COVID-19 vaccination program has turned to the administration of updated, bivalent booster doses to those who have already received a primary vaccine series and, in many cases, 1 or more prior booster dose. Federal health officials have also begun considering future strategies for COVID-19 vaccination, including annual revaccination campaigns using vaccines reformulated to match circulating variants. 35 Yet more than 2 years into the vaccination effort, more than 50 million adults in the US have not completed a primary series, and these individuals remain at a substantially increased risk of hospitalization and death. 36 The causes of this vaccine hesitancy and refusal are varied and extend beyond political beliefs or party affiliation alone. 37 It therefore remains imperative for public health officials to continue and enhance activities intended to improve initial vaccination coverage, in tandem with current or future booster campaigns. To be most effective, these efforts—and corresponding messages—should be tailored to their intended audiences, address the particular sources of vaccine hesitancy among those groups, and seek to include direct participation from members of those communities as trusted ambassadors of provaccine messages. 38 As part of this work, engagement with conservative and Republican leaders, in particular, has been identified as a promising approach to promoting COVID-19 vaccine acceptance. 38

Our study has several limitations. First, there are plausible alternative explanations for the difference in excess death rates by political party affiliation beyond the explanatory role of vaccines discussed herein. Second, our mortality data, although detailed and recent, only included approximately 83.5% of deaths in the US and did not include cause of death. Although overall excess death patterns in our data are similar to those in other reliable sources, such as the Centers for Disease Control and Prevention National Center for Health Statistics data, it is possible that the deaths that our study data did not include may disproportionately occur among individuals registered with a particular political party, potentially biasing our results. In addition, the completeness of our mortality data may vary across states or time, potentially biasing our estimates of excess death rates. Third, all excess death models rely on fundamentally untestable assumptions to construct the baseline number of deaths we would expect in the absence of the COVID-19 pandemic. Fourth, because we did not have information on individual vaccination status, analyses of the association between vaccination rates and excess deaths relied on county-level vaccination rates. Fifth, our study was based on data from 2 states with readily obtainable historical voter registration information (Florida and Ohio); hence, our results may not generalize to other states.

Our study found evidence of higher excess mortality for Republican voters compared with Democratic voters in Florida and Ohio after, but not before, COVID-19 vaccines were available to all adults in the US. These differences in excess death rates were larger in counties with lower vaccination rates. If differences in COVID-19 vaccination by political party affiliation persist, particularly in the absence of other pandemic mitigation strategies, the higher excess death rate observed among Republican voters may continue through subsequent stages of the pandemic.

Accepted for Publication: March 4, 2023.

Published Online: July 24, 2023. doi:10.1001/jamainternmed.2023.1154

Corresponding Author: Jacob Wallace, PhD, Department of Health Policy and Management, Yale School of Public Health, 60 College St, New Haven, CT 06510 ( [email protected] ).

Author Contributions: Drs Wallace and Goldsmith-Pinkham had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: All authors.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Goldsmith-Pinkham.

Obtained funding: Wallace, Schwartz.

Supervision: Wallace.

Conflict of Interest Disclosures: None reported.

Funding/Support: The Tobin Center for Economic Policy at Yale University and the Yale School of Public Health COVID-19 Rapid Response Research Fund funded this study.

Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

Disclaimer: The content is solely the responsibility of the authors and does not necessary reflect the official views of the supporting organizations.

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Related Information

• Comirnaty (COVID-19 Vaccine, mRNA) (2024-2025 Formula)
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• Moderna COVID-19 Vaccine (2024-2025 Formula)
• Pfizer-BioNTech COVID-19 Vaccine (2024-2025 Formula)
• FDA Resources for the Fall Respiratory Illness Season
• Updated COVID-19 Vaccines for Use in the United States Beginning in Fall 2024
• June 5, 2024, Meeting of the Vaccines and Related Biological Products Advisory Committee

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