Early COVID-19 Cases Research Articles

SARS-CoV-2 infection at the Huanan seafood market

The Huanan market harbored many of the early COVID-19 cases in 2019 and is a key element to understanding the origin of the pandemic. Whether the initial animal-to-human transmission did occur at this market is still debated. Here we do not examine how SARS-CoV-2 virus was introduced at the market, but focus on how early cases may have been infected at the market. Based on available evidence, we suggest that several early infections at the Huanan market may have occurred via human-to-human transmission in closed spaces such as canteens, Mahjong rooms or toilets. We advocate for further studies to investigate this hypothesis.

Antibody Response to SARS-CoV-2 in the First Batch of COVID-19 Patients in China by a Self-Developed Rapid IgM-IgG Test.

It has been over two years since the COVID-19 pandemic began and it is still an unprecedented global challenge. Here, we aim to characterize the antibody profile from a large batch of early COVID-19 cases in China, from January – March 2020. More than 1,000 serum samples from participants in Hubei and Zhejiang province were collected. A series of serum samples were also collected along the disease course from 70 patients in Shanghai and Chongqing for longitudinal analysis. The serologic assay (ALLtest) we developed was confirmed to have high sensitivity (92.58% - 97.55%) and high specificity (92.14% - 96.28%) for the detection of SARS-CoV-2 nucleocapsid-specific antibodies. Confirmed cases found in the Hubei Provincial Center for Disease Control and Prevention (HBCDC), showed a significantly (p = 0.0018) higher positive rate from the ALLtest than RNA test. Then, we further identified the disease course, age, sex, and symptoms that were correlating factors with our ALLtest results. In summary, we confirmed the high reliability of our ALLtest and its important role in COVID-19 diagnosis. The correlating factors we identified will require special attention during future clinical application.

Identification of the first COVID-19 infections in the US using a retrospective analysis (REMEDID)

Accurate detection of early COVID-19 cases is crucial to reduce infections and deaths, however, it remains a challenge. Here, we used the results from a seroprevalence study in 50 US states to apply our Retrospective Methodology to Estimate Daily Infections from Deaths (REMEDID) with the aim of analyzing the initial spread of SARS-CoV-2 infections across the US. Our analysis revealed that the virus likely entered the country through California on December 28, 2019, which corresponds to 16 days prior to the officially recognized entry date established by the Centers of Disease Control and Prevention. Furthermore, the REMEDID algorithm provides evidence that SARS-CoV-2 entered, on average, a month earlier than previously reflected in official data for each US state. Collectively, our mathematical modeling provides more accurate estimates of the initial COVID-19 cases in the US, and has the ability to be extrapolated to other countries and used to retrospectively track the progress of the pandemic. The use of approaches such as REMEDID are highly recommended to better understand the early stages of an outbreak, which will enable health authorities to improve mitigation and preventive measures in the future.

An Analytical Study of the Early COVID-19 Cases in Jordan

Background: The daily cases and fatalities in the COVID-19 pandemic are still on the rise. This study aimed to examine the epidemiological trends of the COVID-19 patients at an early stage in the pandemic. Methods: This study included all COVID-19 positive patients who were admitted to the Prince Hamza Hospital between the 2nd and 31st of March 2020. The demographic, clinical, and laboratory data of the COVID-19 positive patients were reviewed and analysed.Results: Of the 132 patients admitted during the period of study, 81 were males, with a mean age of 38.9 years (SD ± 19 yrs). Of those, 55 (41.7%) had comorbidities, 9% required oxygen supplementation, and 3 (2.2%) died. The most common clinical manifestations on admission were fever (36.4%), dry cough (17.4%), shortness of breath (12.9%), and anosmia (10.6%). CRP was elevated in 45.4%, and ESR was also high in 22.7% of patients with CRP elevation and was statistically significant in symptomatic patients (p = 0.004). Likewise, D-dimer was markedly elevated in 42.1% of the symptomatic patients (p = 0.025). Chest CT scans in symptomatic patients showed statistically significant bilateral lung infection (17.2%) as compared to non-symptomatic patients (p = 0.049). Conclusion: The COVID-19 outbreak in Jordan at the beginning of the pandemic had presented clinically significant symptoms associated with higher values of CRP and CT chest findings.

Twelve-Month Follow-up of Early COVID-19 Cases in the United States: Cellular and Humoral Immune Longevity.

We quantify antibody and memory B-cell responses to severe acute respiratory syndrome coronavirus 2 at 6 and 12 months postinfection among 7 unvaccinated US coronavirus disease 2019 cases. All had detectable S-specific memory B cells and immunoglobulin G at both time points, with geometric mean titers of 117.2 BAU/mL and 84.0 BAU/mL at 6 and 12 months, respectively.

National Vulnerability to Pandemics: The Role of Macroenvironmental Factors in COVID-19 Evolution.

The aim of this research is to show that vulnerability to pandemics is unequal across nations, and that culture besides other national factors helps unleash some of the disparities. A nation's vulnerability to pandemics is defined as a state of fragility and dereliction at the national level perceptible at the early stage of the emergence of a pathogen when no definite information is available about it and no clear response is in place, creating a dependence on national factors as well as contextual factors. That is, vulnerability reflects the evolution or spread of a nascent pandemic in a given nation. A set of hypotheses that prescribe how a nation's factors would contribute to its vulnerability is developed. Data reflecting the national factors of a sample of countries that reported early COVID-19 cases were collected from secondary sources to test the hypotheses. The results show that, whereas factors such as economy and healthcare had a modest effect, two cultural factors were salient in shaping a nation's vulnerability to COVID-19. Furthermore, poverty prevalence associated with a nation's vulnerability. Delineating how a nation's culture and macroenvironmental factors shape its vulnerability at early stages of pandemic evolution, the results encourage policymakers to extend timely support to nations high on uncertainty avoidance and low on indulgence, as well as where poverty is prevalent. Such nations require proactive measures such as behavioral interventions and communications that are culturally sensitive and inclusive.

ZRC3308 Monoclonal Antibody Cocktail Shows Protective Efficacy in Syrian Hamsters against SARS-CoV-2 Infection.

We have developed a monoclonal antibody (mAb) cocktail (ZRC-3308) comprising of ZRC3308-A7 and ZRC3308-B10 in the ratio 1:1 for COVID-19 treatment. The mAbs were designed to have reduced immune effector functions and increased circulation half-life. mAbs showed good binding affinities to non-competing epitopes on RBD of SARS-CoV-2 spike protein and were found neutralizing SARS-CoV-2 variants B.1, B.1.1.7, B.1.351, B.1.617.2, and B.1.617.2 AY.1 in vitro. The mAb cocktail demonstrated effective prophylactic and therapeutic activity against SARS-CoV-2 infection in Syrian hamsters. The antibody cocktail appears to be a promising candidate for prophylactic use and for therapy in early COVID-19 cases that have not progressed to severe disease.

Dissecting the early COVID-19 cases in Wuhan.

Elucidating the origin of the pandemic requires understanding of the Wuhan outbreak.

Visualizing Spatiotemporal Epidemic Clusters on a Map-based Dashboard: A case study of early COVID-19 cases in Singapore

Abstract. Spatiotemporal distribution of the epidemic data plays an important role in its understanding and prediction. In order to understand the transmission patterns of infectious diseases in a more intuitive way, many works applied various visualizations to show the epidemic datasets. However, most of them focus on visualizing the epidemic information at the overall level such as the confirmed counts each country, while spending less effort on powering user to effectively understand and reason the very large and complex epidemic datasets through flexible interactions. In this paper, the authors proposed a novel map-based dashboard for visualizing and analyzing spatiotemporal clustering patterns and transmission chains of epidemic data. We used 102 confirmed cases officially reported by the Ministry of Health in Singapore as the test dataset. This experiment shown that the well-designed and interactive map-based dashboard is effective in shorten the time that users required to mine the spatiotemporal characteristics and transmission chains behind the textual and numerical epidemic data.

The association between initial COVID-19 spread and meteorological factors in Indonesia.

On March 2, 2020, the first Coronavirus Disease (COVID-19) case was reported in Jakarta, Indonesia. One and a half months later (15/05/2020), the cumulative number of infection cases was 16,496, with a total of 1076 mortalities. This study investigates the possible role of weather in the early cases of COVID-19 in six selected cities in Indonesia. Daily temperature and relative humidity data from weather stations nearby in each city were collected from March 3 to April 30, 2020, corresponding with COVID-19 incidence. Correlation tests and regression analysis were performed to examine the association of those two data series. Moreover, we analyzed the distribution of COVID-19 referring the weather data to estimate the effective range of weather data supporting the COVID-19 incidence. Our result reveals that weather data is generally associated with COVID-19 incidence. The daily average temperature (T-ave) and relative humidity (RH) present significant positive and negative correlation with COVID-19 data, respectively. However, the correlation coefficients are weak, with the strongest correlations found at the 5-day lag, i.e., 0.37 (− 0.41) for T-ave (RH). The regression analysis consistently confirmed this relation. The distribution analysis reveals that most COVID-19 cases in Indonesia occurred in the daily temperature range of 25–31 °C and relative humidity of 74–92%. Our findings suggest that COVID-19 incidence in Indonesia has a weak association with weather conditions. Therefore, non-meteorological factors seem to play a more prominent role and should be given greater consideration in preventing the spread of COVID-19.Graphic abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s42398-021-00202-9.

Mortality Analysis of Early COVID-19 Cases in the Philippines Based on Observed Demographic and Clinical Characteristics

This study aims to determine the demographic, epidemiologic, and clinical characteristics of COVID-19 cases that are highly susceptible to COVID-19 infection, with longer hospitalization and at higher risk of mortality and to provide insights that may be useful to assess the vaccination priority program and allocate hospital resources. Methods that were used include descriptive statistics, nonparametric analysis, and survival analysis. Results of the study reveal that women are more susceptible to infection while men are at risk of longer hospitalization and higher mortality. Significant risk factors to COVID-19 mortality are older age, male sex, difficulty breathing, and comorbidities like hypertension and diabetes. Patients with these combined symptoms should be considered for admission to the COVID-19 facility for proper management and care. Also, there is a significant delay in the testing and diagnosis of those who died, implying that timeliness in the testing and diagnosis of patients is crucial in patient survival.

Unknown Disease Outbreaks Detection: A Pilot Study on Feature-Based Knowledge Representation and Reasoning Model.

Background: The outbreak of COVID-19 in 2019 has rapidly swept the world, causing irreparable loss to human beings. The pandemic has shown that there is still a delay in the early response to disease outbreaks and needs a method for unknown disease outbreak detection. The study's objective is to establish a new medical knowledge representation and reasoning model, and use the model to explore the feasibility of unknown disease outbreak detection.Methods: The study defined abnormal values with diagnostic significances from clinical data as the Features, and defined the Features as the antecedents of inference rules to match with knowledge bases, achieved in detecting known or emerging infectious disease outbreaks. Meanwhile, the study built a syndromic surveillance base to capture the target cases' Features to improve the reliability and fault-tolerant ability of the system.Results: The study combined the method with Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and early COVID-19 outbreaks as empirical studies. The results showed that with suitable surveillance guidelines, the method proposed in this study was capable to detect outbreaks of SARS, MERS, and early COVID-19 pandemics. The quick matching accuracies of confirmed infection cases were 89.1, 26.3–98%, and 82%, and the syndromic surveillance base would capture the Features of the remaining cases to ensure the overall detection accuracies. Based on the early COVID-19 data in Wuhan, this study estimated that the median time of the early COVID-19 cases from illness onset to local authorities' responses could be reduced to 7.0–10.0 days.Conclusions: This study offers a new solution to transfer traditional medical knowledge into structured data and form diagnosis rules, enables the representation of doctors' logistic thinking and the knowledge transmission among different users. The results of empirical studies demonstrate that by constantly inputting medical knowledge into the system, the proposed method will be capable to detect unknown diseases from existing ones and perform an early response to the initial outbreaks.

Clinico-epidemiological profile of early cases of Covid-19 in state of Haryana, India

Background: Covid -19 disease is caused by novel coronavirus known as SARS CoV 2. Coronaviruses are known to cause disease in humans which can be a common cold or a serious pneumonia. SARS Cov 2 is a new variant of coronavirus which was never reported in humans before detection of cluster of cases of pneumonia in Wuhan on 31st December, 2019.Objective: The study was conducted with the objective to find out clinico- epidemiological profiles of early Covid-19 patients in state of Haryana, India and to find out knowledge about covid appropriate behaviors among covid 19 patients. Methods: All the covid-19 cases in the five districts of Haryana were enumerated from March 2020 to May 2020. These Covid-19 cases were contacted telephonically and those who gave their consent to participate in the study were asked to fill interview schedule. Observations: There were 356 covid cases reported in five districts attached to PGIMS, Rohtak. Out of total,254 subjects participated in the study, there were 60.6 % males and 39.4% females who participated in the study. Blood groups of all the participants were also enquired and it was observed that B +ve was most common (19.7%) blood group followed by A+ve (16.5%) and O +ve (15.7%). Conclusion: It can be concluded by the study that majority of the subjects were males, overweight and blood group A & B were the most common blood groups. It was observed that 75.2 % participants were aware about the use of mask, social distancing, hand hygiene and cough etiquettes.

Possible Roles of Permafrost Melting, Atmospheric Transport, and Solar Irradiance in the Development of Major Coronavirus and Influenza Pandemics.

Major pandemics involving respiratory viruses develop semi-regularly and require a large flux of novel viruses, yet their origination is equivocal. This paper explores how natural processes could give rise to this puzzling combination of characteristics. Our model is based on available data regarding the emergence of historic influenzas, early COVID-19 cases and spreading, the microbiome of permafrost, long-distance airborne transport of viruses reaching stratospheric levels, ultraviolet immunosuppression, sunlight variations, weather patterns, Arctic thawing, and global warming. Atmospheric conveyance is supported by hemispheric distribution disparities, ties of COVID-19 cases to air pollution particulate concentrations, and contemporaneous animal infections. The following sequence is proposed: (1) virus emergence after hot Arctic summers, predominantly near solar irradiance maxima or involving wildfires, indicates release of large amounts of ancient viruses during extensive permafrost melting, which are then incorporated in autumn polar air circulation, where cold storage and little sunlight permit survival. (2) Pandemics onset in winter to spring at rather few locations: from climate data on Wuhan, emergence occurs where the North Polar Jet stream hovers while intersecting warmer, moist air, producing rain which deposits particulates with the viral harvest on a vulnerable human population. (3) Spring and summer increases in COVID-19 cases link to high solar irradiance, implicating ultraviolet immune suppression as one means of amplification. (4) Viruses multiplied by infected humans at close range being incorporated in atmospheric circulation explains rapid global spread, periodic case surges (waves), and multi-year durations. Pollution and wind geography affect uptake and re-distribution. Our model can be tested, e.g., against permafrost stored in laboratories as well as Artic air samples, and suggests mitigating actions.

COVID-19 survivor experiencing long-term symptoms

COVID-19 survivor experiencing long-term symptoms

Prevalence of interstitial pneumonia suggestive of COVID-19 at 18F-FDG PET/CT in oncological asymptomatic patients in a high prevalence country during pandemic period: a national multi-centric retrospective study

PurposeTo assess the presence and pattern of incidental interstitial lung alterations suspicious of COVID-19 on fluorine-18-fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT) ([18F]FDG PET/CT) in asymptomatic oncological patients during the period of active COVID-19 in a country with high prevalence of the virus.MethodsThis is a multi-center retrospective observational study involving 59 Italian centers. We retrospectively reviewed the prevalence of interstitial pneumonia detected during the COVID period (between March 16 and 27, 2020) and compared to a pre-COVID period (January–February 2020) and a control time (in 2019). The diagnosis of interstitial pneumonia was done considering lung alterations of CT of PET.ResultsOverall, [18F]FDG PET/CT was performed on 4008 patients in the COVID period, 19,267 in the pre-COVID period, and 5513 in the control period. The rate of interstitial pneumonia suspicious for COVID-19 was significantly higher during the COVID period (7.1%) compared with that found in the pre-COVID (5.35%) and control periods (5.15%) (p < 0.001). Instead, no significant difference among pre-COVID and control periods was present. The prevalence of interstitial pneumonia detected at PET/CT was directly associated with geographic virus diffusion, with the higher rate in Northern Italy. Among 284 interstitial pneumonia detected during COVID period, 169 (59%) were FDG-avid (average SUVmax of 4.1).ConclusionsA significant increase of interstitial pneumonia incidentally detected with [18F]FDG PET/CT has been demonstrated during the COVID-19 pandemic. A majority of interstitial pneumonia were FDG-avid. Our results underlined the importance of paying attention to incidental CT findings of pneumonia detected at PET/CT, and these reports might help to recognize early COVID-19 cases guiding the subsequent management.

Characteristics of First Cases of Coronavirus Disease 2019 and the Effort to Prevent the Early Spread of COVID-19 in Saudi Arabia.

PurposeTo characterize the overall trends in early cases of COVID-19 and to identify the key points of the government effort to minimize the infection.Patients and MethodsA retrospective review and data were retrieved through online sources and the Saudi Ministry of Health daily announcements that were available online. The data included the number of infections per day, and the gender, nationality, location, source of infection, incidence, rate of recovery, and the rate mortality of COVID-19 patients in Saudi Arabia between March 1 and March 16, 2020.ResultsThe incidence of COVID-19 increased in the first two weeks in Saudi Arabia, from zero cases on March 1 to more than 15 cases per day on March 16, with a total of 133 cases. The majority of patients were males (54.9%), of Saudi descent (54.9%), and had travel as their source of infection (57.1%). Most of the cases were in Makah (37.6%); however, there were increases in cases in all cities. Moreover, the Saudi government enacted several steps to minimize the spreading of infection. There was no statistical significance between source of infection with gender (p = 0.323). However, there was statistical significance between source of infection and nationality (p < 0.001).ConclusionThe incidence of COVID-19 cases is expected to continue to increase. However, the efforts of the Saudi government are crucial in minimizing the spread of this infection.

Mortality among the early cases of COVID-19 and its association with key comorbidities in a large state in South India

Background: The mortality associated with the pandemic COVID-19 is a subject of intense scrutiny as COVID-19 can cause severe disease leading to hospitalization in ICU and potentially death, especially in the elderly with comorbidities. A statistical analysis is carried out to study the impact of age, gender and comorbidities on deaths among early one lakh infected population of Karnataka, a large state in south India.Methods: Daily case fatality rate and adjusted case fatality rate (CFR) (adjusted to median death time) are estimated. The impacts of age, gender and comorbidities on mortality outcomes of COVID patients are studied.Results: The daily CFR on 27th July for Karnataka is estimated from the dataset to be 1.93%. However, the adjusted CFR based on the median number of days from diagnosis to death was found to be 2.15% (95% confidence interval 2%-2.3%) on that day. The deaths among male patients outnumber those in females. As far as age of the patients is concerned, more than 50% of the deaths occurred in the age group 50-60 and 60-70 years. Majority of deaths reported in the state were associated with at least one of the comorbidity. Diabetes mellitus and hypertension were the most significant comorbidities.Conclusions: The daily adjusted CFR for the study region is found to be lower than the CFR of the whole nation. Also the age, gender and comorbidities were found to be associated with the deaths as opposed to the infection alone. It was also deduced that, patients with a history of diabetes or hypertension or ischemic heart disease or a combination of any of these were most likely to experience severe outcomes of the infection.

Tailored intelligence to detect unusual epidemic activity following the explosion at Vector, Russia

We used open source data from the Epiwatch observatory to monitor for early disease signals in Russia and surrounding countries following an explosion at a BSL 4 laboratory, Vector, in Siberia in September 2019. Upon news of the explosion at Vector on September 16th 2019, the Epiwatch team added the Russian language and key words Russia, Siberia, Novosibirsk, and Koltsovo to the Standard Operating Procedures, in addition to the usual epidemic-specific keywords used in Epiwatch. We also searched for outbreak reports in countries bordering Siberia, including Mongolia, Kazakhstan and China. Given local spread of an epidemic could manifest in these countries, we included searching in Chinese, Mongolian and Kazakh. We added “Ukraine” as a key word, given current conflict between Russia and Ukraine. Data collection began in September 2019, one week after the explosion, with this considered the baseline. We demonstrate a method for rapid epidemic intelligence following an incident of concern, the explosion at Vector. There were some unexplained outbreaks in Russia in the three months following the explosion. No unexplained outbreaks were detected in countries bordering Russia, nor in Ukraine in the three months following the explosion. We detected an accidental release of brucella from a laboratory in China in early December 2019 and two reports of severe pneumonia prior to official reports, which could have been early COVID-19 cases. Best practice in preparedness should include surveillance for disease events in the months following an event of concern at local, national and global levels. In the absence of official surveillance data, open source intelligence may be the only available means of detecting outbreaks and enabling early response and mitigation for the rest of the world. Epiwatch was able to identify reports of Russian outbreaks in the weeks and months following the Vector explosion, which allowed monitoring of outbreaks of concern without a known cause.

Practical Recommendation of COVID-19 for Otorhinolaryngologists

Otorhinolaryngologic symptoms such as cough, rhinorrhea, anosmia, ageusia, and fever can precede development of COVID-19. Otolaryngologists are vulnerable to potential COVID-19 infection because early cases of COVID-19 have been asymptomatic or show mild manifestations. Otolaryngologists should wear adequate personal protective equipment (PPE) according as location or situation. PPE includes medical masks, goggles, and fluid-resistant gown and gloves. Authors provide clinical recommendations based on PPE for otorhinolaryngologists during the COVID-19 pandemic. In addition, we reviewed the literature related to clinical guidelines of foreign otorhinolaryngology societies with regard to COVID-19.