Medicine / Special Report

Vol. 5, NO. 1 / April 2020

The Epidemiology of COVID-19 Transmission

The Editors

Letters to the Editors

In response to “The Epidemiology of COVID-19 Transmission”

← SARS-CoV-2: Research Guide

The following list of papers and articles will be updated regularly and is offered here as a resource for researchers and interested readers.


  • Dynamic Causal Modelling of COVID-19 (Preprint)

    Karl Friston et al. / May 28, 2020

    This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations—to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.

    Karl J. Friston et al., "Dynamic Causal Modelling of COVID-19," arXiv (2020), doi:10.12688/wellcomeopenres.15881.1.

  • Susceptible Supply Limits the Role of Climate in the Early SARS-CoV-2 Pandemic

    Rachel E. Baker et al. / May 18, 2020

    Preliminary evidence suggests that climate may modulate the transmission of SARS-CoV-2. Yet it remains unclear whether seasonal and geographic variations in climate can substantially alter the pandemic trajectory, given high susceptibility is a core driver. Here, we use a climate-dependent epidemic model to simulate the SARS-CoV-2 pandemic probing different scenarios based on known coronavirus biology. We find that while variations in weather may be important for endemic infections, during the pandemic stage of an emerging pathogen the climate drives only modest changes to pandemic size. A preliminary analysis of non-pharmaceutical control measures indicates that they may moderate the pandemic-climate interaction via susceptible depletion. Our findings suggest, without effective control measures, strong outbreaks are likely in more humid climates and summer weather will not substantially limit pandemic growth.

    Rachel E. Baker et al., "Supply Limits the Role of Climate in the Early SARS-CoV-2 Pandemic," Science (2020), doi:10.1126/science.abc2535.

  • Estimating the Burden of SARS-CoV-2 in France

    Henrik Salje et al. / May 13, 2020

    France has been heavily affected by the SARS-CoV-2 epidemic and went into lockdown on the 17 March 2020. Using models applied to hospital and death data, we estimate the impact of the lockdown and current population immunity. We find 3.6% of infected individuals are hospitalized and 0.7% die, ranging from 0.001% in those <20 years of age (ya) to 10.1% in those >80ya. Across all ages, men are more likely to be hospitalized, enter intensive care, and die than women. The lockdown reduced the reproductive number from 2.90 to 0.67 (77% reduction). By 11 May 2020, when interventions are scheduled to be eased, we project 2.8 million (range: 1.8–4.7) people, or 4.4% (range: 2.8–7.2) of the population, will have been infected. Population immunity appears insufficient to avoid a second wave if all control measures are released at the end of the lockdown.

    Henrik Salje et al., "Estimating the Burden of SARS-CoV-2 in France," Science (2020), doi:10.1126/science.abc3517.

  • Epidemiology and Transmission of COVID-19 in 391 Cases and 1286 of Their Close Contacts in Shenzhen, China: a Retrospective Cohort Study

    Qifang Bi et al. / April 27, 2020

    Background: Rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Wuhan, China, prompted heightened surveillance in Shenzhen, China. The resulting data provide a rare opportunity to measure key metrics of disease course, transmission, and the impact of control measures.
    Methods: From Jan 14 to Feb 12, 2020, the Shenzhen Center for Disease Control and Prevention identified 391 SARS-CoV-2 cases and 1286 close contacts. We compared cases identified through symptomatic surveillance and contact tracing, and estimated the time from symptom onset to confirmation, isolation, and admission to hospital. We estimated metrics of disease transmission and analysed factors influencing transmission risk.
    Findings: Cases were older than the general population (mean age 45 years) and balanced between males (n=187) and females (n=204). 356 (91%) of 391 cases had mild or moderate clinical severity at initial assessment. As of Feb 22, 2020, three cases had died and 225 had recovered (median time to recovery 21 days; 95% CI 20–22). Cases were isolated on average 4·6 days (95% CI 4·1–5·0) after developing symptoms; contact tracing reduced this by 1·9 days (95% CI 1·1–2·7). Household contacts and those travelling with a case were at higher risk of infection (odds ratio 6·27 [95% CI 1·49–26·33] for household contacts and 7·06 [1·43–34·91] for those travelling with a case) than other close contacts. The household secondary attack rate was 11·2% (95% CI 9·1–13·8), and children were as likely to be infected as adults (infection rate 7·4% in children <10 years vs population average of 6·6%). The observed reproductive number (R) was 0·4 (95% CI 0·3–0·5), with a mean serial interval of 6·3 days (95% CI 5·2–7·6).
    Interpretation: Our data on cases as well as their infected and uninfected close contacts provide key insights into the epidemiology of SARS-CoV-2. This analysis shows that isolation and contact tracing reduce the time during which cases are infectious in the community, thereby reducing the R. The overall impact of isolation and contact tracing, however, is uncertain and highly dependent on the number of asymptomatic cases. Moreover, children are at a similar risk of infection to the general population, although less likely to have severe symptoms; hence they should be considered in analyses of transmission and control.

    Qifang Bi et al., “Epidemiology and Transmission of COVID-19 in 391 Cases and 1286 of Their Close Contacts in Shenzhen, China: a Retrospective Cohort Study,” The Lancet Infectious Diseases (2020), doi:10.1016/S1473-3099(20)30287-5.

  • COVID-19 and the Need of Targeted Inverse Quarantine

    Fabian Standl, Karl-Heinz Jöckel and Andreas Stang / April 24, 2020

    The recommendation of social isolation of the whole population as currently practiced in Germany is driven by the idea that the spread of the disease is reduced which may prevent a sudden overcrowding of hospitals with seriously ill COVID-19 cases as has been observed in Northern Italy. Sebastiani et al. [3] in this issue show that the strict isolation measures as taken in Lombardy and later on in all over Italy was associated with the reduction of progression of the epidemic. This approach has enormous negative economic and societal consequences [4] but may be justified in the beginning of an epidemic when infection rates, hospitalization rates and case-fatality cannot be stratified by potential determinants. As soon as more detailed data on the spread and case-fatality of the corona infections is available, a targeted, that is, risk-adapted approach to prevent corona infections is possible and should be implemented because social isolation of the entire population will lead to unsustainable conditions in the population in the long run. For a targeted strategy to prevent deaths from COVID-19, a high-risk approach is possible and does not have the enormous negative economic and societal consequences.

    Fabian Standl, Karl-Heinz Jöckel and Andreas Stang, “COVID-19 and the Need of Targeted Inverse Quarantine,” European Journal of Epidemiology (2020), doi:10.1007/s10654-020-00629-0.

  • An Improved Mathematical Prediction of the Time Evolution of the COVID-19 Pandemic in Italy, with Monte Carlo Simulations and Error Analyses (Preprint)

    Ignazio Ciufolini and Antonio Paolozzi / April 23, 2020

    Here we present an improved mathematical analysis of the time evolution of the Covid-19 pandemic in Italy and a statistical error analyses of its evolution, including Monte Carlo simulations with a very large number of runs to evaluate the uncertainties in its evolution. A previous analysis was based on the assumption that the number of nasopharyngeal swabs would be constant, however the number of daily swabs has been increasing with an average factor of about four with respect to our previous analysis, Therefore, here we consider the time evolution of the ratio of diagnosed positive cases to number of swabs, which is more representative of the evolution of the pandemic when the number of swabs is increasing or changing in time. We then consider five possible cases for the number of daily swabs and we then estimate the potential dates of a substantial reduction in the number of diagnosed positive cases. We then perform a number of Monte Carlo simulations to evaluate the uncertainty in the prediction of the date of a substantial reduction in the number of diagnosed daily cases. Finally, we present an alternative method to evaluate the uncertainty in our mathematical predictions based on the study of each region of Italy and we present an application of the Central Limit Theorem to display the uncertainty in our mathematical predictions based on the analysis of each region.

    Ignazio Ciufolini and Antonio Paolozzi, “An Improved Mathematical Prediction of the Time Evolution of the COVID-19 Pandemic in Italy, with Monte Carlo Simulations and Error Analyses,” MedRxiv (2020), doi:10.1101/2020.04.20.20073155.

  • Cluster of COVID-19 in Northern France: A Retrospective Closed Cohort Study (Preprint)

    Arnaud Fontanet et al. / April 23, 2020

    The Oise department in France has been heavily affected by COVID-19 in early 2020. Methods: Between 30 March and 4 April 2020, we conducted a retrospective closed cohort study among pupils, their parents and siblings, as well as teachers and non-teaching staff of a high-school located in Oise. Participants completed a questionnaire that covered history of fever and/or respiratory symptoms since 13 January 2020 and had blood tested for the presence of anti-SARS-CoV-2 antibodies. The infection attack rate (IAR) was defined as the proportion of participants with confirmed SARS-CoV-2 infection based on antibody detection. Blood samples from two blood donor centres collected between 23 and 27 March 2020 in the Oise department were also tested for presence of anti-SARS-CoV-2 antibodies. Findings: Of the 661 participants (median age: 37 years), 171 participants had anti-SARS-CoV-2 antibodies. The overall IAR was 25.9% (95% confidence interval (CI) = 22.6-29.4), and the infection fatality rate was 0% (one-sided 97.5% CI = 0-2.1). Nine of the ten participants hospitalised since mid-January were in the infected group, giving a hospitalisation rate of 5.3% (95% CI = 2.4-9.8). Anosmia and ageusia had high positive predictive values for SARS-CoV-2 infection (84.7% and 88.1%, respectively). Smokers had a lower IAR compared to non-smokers (7.2% versus 28.0%, P <0.001). The proportion of infected individuals who had no symptoms during the study period was 17.0% (95% CI = 11.2-23.4). The proportion of donors with anti-SARS-CoV-2 antibodies in two nearby blood banks of the Oise department was 3.0% (95% CI = 1.1-6.4). Interpretation: The relatively low IAR observed in an area where SARS-CoV-2 actively circulated weeks before confinement measures indicates that establishing herd immunity will take time, and that lifting these measures in France will be long and complex.

    Fontanet, Arnaud, et al., “Cluster of COVID-19 in Northern France: A Retrospective Closed Cohort Study,” MedRxiv (2020), doi:10.1101/2020.04.18.20071134.

  • Suppression of COVID-19 Outbreak in the Municipality of Vo, Italy (Preprint)

    Enrico Lavezzo et al. / April 18, 2020

    On the 21st of February 2020 a resident of the municipality of Vo, a small town near Padua, died of pneumonia due to SARS-CoV-2 infection. This was the first COVID-19 death detected in Italy since the emergence of SARS-CoV-2 in the Chinese city of Wuhan, Hubei province. In response, the regional authorities imposed the lockdown of the whole municipality for 14 days. We collected information on the demography, clinical presentation, hospitalization, contact network and presence of SARS-CoV-2 infection in nasopharyngeal swabs for 85.9% and 71.5% of the population of Vo at two consecutive time points. On the first survey, which was conducted around the time the town lockdown started, we found a prevalence of infection of 2.6% (95% confidence interval (CI) 2.1-3.3%). On the second survey, which was conducted at the end of the lockdown, we found a prevalence of 1.2% (95% CI 0.8-1.8%). Notably, 43.2% (95% CI 32.2-54.7%) of the confirmed SARS-CoV-2 infections detected across the two surveys were asymptomatic. The mean serial interval was 6.9 days (95% CI 2.6-13.4). We found no statistically significant difference in the viral load (as measured by genome equivalents inferred from cycle threshold data) of symptomatic versus asymptomatic infections (p-values 0.6 and 0.2 for E and RdRp genes, respectively, Exact Wilcoxon-Mann-Whitney test). Contact tracing of the newly infected cases and transmission chain reconstruction revealed that most new infections in the second survey were infected in the community before the lockdown or from asymptomatic infections living in the same household. This study sheds new light on the frequency of asymptomatic SARS-CoV-2 infection and their infectivity (as measured by the viral load) and provides new insights into its transmission dynamics, the duration of viral load detectability and the efficacy of the implemented control measures.

    Enrico Lavezzo et al., “Suppression of COVID-19 Outbreak in the Municipality of Vo, Italy,” medRxiv (2020), doi:10.1101/2020.04.17.20053157.

  • COVID-19 Antibody Seroprevalence in Santa Clara County, California (Preprint)

    Eran Bendavid et al. / April 17, 2020

    Background Addressing COVID-19 is a pressing health and social concern. To date, many epidemic projections and policies addressing COVID-19 have been designed without seroprevalence data to inform epidemic parameters. We measured the seroprevalence of antibodies to SARS-CoV-2 in Santa Clara County. Methods On 4/3-4/4, 2020, we tested county residents for antibodies to SARS-CoV-2 using a lateral flow immunoassay. Participants were recruited using Facebook ads targeting a representative sample of the county by demographic and geographic characteristics. We report the prevalence of antibodies to SARS-CoV-2 in a sample of 3,330 people, adjusting for zip code, sex, and race/ethnicity. We also adjust for test performance characteristics using 3 different estimates: (i) the test manufacturer's data, (ii) a sample of 37 positive and 30 negative controls tested at Stanford, and (iii) a combination of both. Results The unadjusted prevalence of antibodies to SARS-CoV-2 in Santa Clara County was 1.5% (exact binomial 95CI 1.11-1.97%), and the population-weighted prevalence was 2.81% (95CI 2.24-3.37%). Under the three scenarios for test performance characteristics, the population prevalence of COVID-19 in Santa Clara ranged from 2.49% (95CI 1.80-3.17%) to 4.16% (2.58-5.70%). These prevalence estimates represent a range between 48,000 and 81,000 people infected in Santa Clara County by early April, 50-85-fold more than the number of confirmed cases. Conclusions The population prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that the infection is much more widespread than indicated by the number of confirmed cases. Population prevalence estimates can now be used to calibrate epidemic and mortality projections.

    Eran Bendavid et al., “COVID-19 Antibody Seroprevalence in Santa Clara County, California,” MedRxiv (2020), doi:10.1101/2020.04.14.20062463.

  • The Geographic Spread of COVID-19 Correlates with Structure of Social Networks as Measured by Facebook (Preprint)

    Theresa Kuchler, Dominic Russel, and Johannes Stroebel / April 7, 2020

    We use anonymized and aggregated data from Facebook to show that areas with stronger social ties to two early COVID-19 "hotspots" (Westchester County, NY, in the U.S. and Lodi province in Italy) generally have more confirmed COVID-19 cases as of March 30, 2020. These relationships hold after controlling for geographic distance to the hotspots as well as for the income and population density of the regions. These results suggest that data from online social networks may prove useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19.

    Theresa Kuchler, Dominic Russel, and Johannes Stroebel, “The Geographic Spread of COVID-19 Correlates with Structure of Social Networks as Measured by Facebook,” arXiv:2004.03055 (2020).

  • Critical Community Size for COVID-19 — a Model Based Approach to Provide a Rationale Behind the Lockdown (Preprint)

    Sarmistha Das et al. / April 7, 2020

    Restrictive mass quarantine or lockdown has been implemented as the most important controlling measure to fight against COVID-19. Many countries have enforced 2 - 4 weeks' lockdown and are extending the period depending on their current disease scenario. Most probably the 14-day period of estimated communicability of COVID-19 prompted such decision. But the idea that, if the susceptible population drops below certain threshold, the infection would naturally die out in small communities after a fixed time (following the outbreak), unless the disease is reintroduced from outside, was proposed by Bartlett in 1957. This threshold was termed as Critical Community Size (CCS). Methods: We propose an SEIR model that explains COVID-19 disease dynamics. Using our model, we have calculated country-specific expected time to extinction (TTE) and CCS that would essentially determine the ideal number of lockdown days required and size of quarantined population. Findings: With the given country-wise rates of death, recovery and other parameters, we have identified that, if at a place the total number of susceptible population drops below CCS, infection will cease to exist after a period of TTE days, unless it is introduced from outside. But the disease will almost die out much sooner. We have calculated the country-specific estimate of the ideal number of lockdown days. Thus, smaller lockdown phase is sufficient to contain COVID-19. On a cautionary note, our model indicates another rise in infection almost a year later but on a lesser magnitude.

    Sarmistha Das et al., “Critical Community Size for COVID-19 — a Model Based Approach to Provide a Rationale Behind the Lockdown,” arXiv:2004.03126 (2020).

  • Virological Assessment of Hospitalized Patients with COVID-2019

    Roman Wölfel et al. / April 1, 2020

    Coronavirus disease 2019 (COVID-19) is an acute respiratory tract infection that emerged in late 2019. Initial outbreaks in China involved 13.8% cases with severe, and 6.1% with critical courses. This severe presentation corresponds to the usage of a virus receptor that is expressed predominantly in the lung. By causing an early onset of severe symptoms, this same receptor tropism is thought to have determined pathogenicity, but also aided the control, of severe acute respiratory syndrome (SARS) in 2003. However, there are reports of COVID-19 cases with mild upper respiratory tract symptoms, suggesting the potential for pre- or oligosymptomatic transmission. There is an urgent need for information on body site-specific virus replication, immunity, and infectivity. Here we provide a detailed virological analysis of nine cases, providing proof of active virus replication in upper respiratory tract tissues. Pharyngeal virus shedding was very high during the first week of symptoms (peak at 7.11 × 108 RNA copies per throat swab, day 4). Infectious virus was readily isolated from throat- and lung-derived samples, but not from stool samples, in spite of high virus RNA concentration. Blood and urine never yielded virus. Active replication in the throat was confirmed by viral replicative RNA intermediates in throat samples. Sequence-distinct virus populations were consistently detected in throat and lung samples from the same patient, proving independent replication. Shedding of viral RNA from sputum outlasted the end of symptoms. Seroconversion occurred after 7 days in 50% of patients (14 days in all), but was not followed by a rapid decline in viral load. COVID-19 can present as a mild upper respiratory tract illness. Active virus replication in the upper respiratory tract puts the prospects of COVID-19 containment in perspective.

    Roman Wölfel et al., “Virological Assessment of Hospitalized Patients with COVID-2019,” Nature (2020), doi:10.1038/s41586-020-2196-x.

  • COVID-19: Attacks the 1-Beta Chain of Hemoglobin and Captures the Porphyrin to Inhibit Human Heme Metabolism (Preprint)

    Liu Wenzhong and Li Hualan / March 30, 2020

    The novel coronavirus pneumonia (COVID-19) is an infectious acute respiratory infection caused by the novel coronavirus. The virus is a positive-strand RNA virus with high homology to bat coronavirus. In this study, conserved domain analysis, homology modeling, and molecular docking were used to compare the biological roles of certain proteins of the novel coronavirus. The results showed the ORF8 and surface glycoprotein could bind to the porphyrin, respectively. At the same time, orf1ab, ORF10, and ORF3a proteins could coordinate attack the heme on the 1-beta chain of hemoglobin to dissociate the iron to form the porphyrin. The attack will cause less and less hemoglobin that can carry oxygen and carbon dioxide. The lung cells have extremely intense poisoning and inflammatory due to the inability to exchange carbon dioxide and oxygen frequently, which eventually results in ground-glass-like lung images. The mechanism also interfered with the normal heme anabolic pathway of the human body, is expected to result in human disease. According to the validation analysis of these finds, chloroquine could prevent orf1ab, ORF3a, and ORF10 to attack the heme to form the porphyrin, and inhibit the binding of ORF8 and surface glycoproteins to porphyrins to a certain extent, effectively relieve the symptoms of respiratory distress. Favipiravir could inhibit the envelope protein and ORF7a protein bind to porphyrin, prevent the virus from entering host cells, and catching free porphyrins. Because the novel coronavirus is dependent on porphyrins, it may originate from an ancient virus. Therefore, this research is of high value to contemporary biological experiments, disease prevention, and clinical treatment.

    Liu Wenzhong and Li Hualan, “COVID-19: Attacks the 1-Beta Chain of Hemoglobin and Captures the Porphyrin to Inhibit Human Heme Metabolism,” ChemRxiv, March 30, 2020, doi:10.26434/chemrxiv.11938173.v5.

  • Analysis on Epidemic Situation and Spatiotemporal Changes of COVID-19 in Anhui

    Meng Liu et al. / March 30, 2020

    We used the epidemic data of COVID-19 published on the official website of the municipal health commission in Anhui province. We mapped the spatiotemporal changes of confirmed cases, fitted the epidemic situation by the population growth curve at different stages and took statistical description and analysis of the epidemic situation in Anhui province. It was found that the cumulative incidence of COVID-19 was 156/100 000 by February 18, 2020 and the trend of COVID-19 epidemic declined after February 7, changing from J curve to S curve. The actual number of new cases began to decrease from February 2 to February 4 due to the time of case report and actual onset delayed by 3 to 5 days.

    Meng Liu et al., “Analysis on Epidemic Situation and Spatiotemporal Changes of COVID-19 in Anhui, [安徽省新型冠状病毒肺炎流行与疫情时空变化分析],” Chinese Journal of Preventive Medicine 54 (2020): E019, doi:10.3760/cma.j.cn112150-20200221-00150.

  • Estimates of the Severity of Coronavirus Disease 2019: A Model-based Analysis

    Robert Verity et al. / March 30, 2020

    Background
    In the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases.
    Methods
    We collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation.
    Findings
    Using data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9–19·2) and to hospital discharge to be 24·7 days (22·9–28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70?117), we estimated a crude case fatality ratio (adjusted for censoring) of 3·67% (95% CrI 3·56–3·80). However, after further adjusting for demography and under-ascertainment, we obtained a best estimate of the case fatality ratio in China of 1·38% (1·23–1·53), with substantially higher ratios in older age groups (0·32% [0·27–0·38] in those aged <60 years vs 6·4% [5·7–7·2] in those aged ?60 years), up to 13·4% (11·2–15·9) in those aged 80 years or older. Estimates of case fatality ratio from international cases stratified by age were consistent with those from China (parametric estimate 1·4% [0·4–3·5] in those aged <60 years [n=360] and 4·5% [1·8–11·1] in those aged ?60 years [n=151]). Our estimated overall infection fatality ratio for China was 0·66% (0·39–1·33), with an increasing profile with age. Similarly, estimates of the proportion of infected individuals likely to be hospitalised increased with age up to a maximum of 18·4% (11·0–7·6) in those aged 80 years or older.
    Interpretation
    These early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death.

    Robert Verity et al., “Estimates of the Severity of Coronavirus Disease 2019: A Model-based Analysis,” The Lancet: Infectious Diseases (2020), doi:10.1016/S1473-3099(20)30243-7.

  • Mathematical Modeling of Epidemic Diseases; A Case Study of the COVID-19 Coronavirus (Preprint)

    Reza Sameni / March 24, 2020

    The outbreak of the Coronavirus COVID-19 has taken the lives of several thousands worldwide and locked-out many countries and regions, with yet unpredictable global consequences. In this research we study the epidemic patterns of this virus, from a mathematical modeling perspective. The study is based on endemic extensions of the well-known susceptible-infected-recovered (SIR) family of compartmental models. It is shown how social measures such as distancing, regional lock-downs, quarantine and global public health vigilance, influence the model parameters, which can eventually change the mortality rates and active contaminated cases over time, in the real world. As with all mathematical models, the predictive ability of the model is limited by the accuracy of the available data and to the so-called level of abstraction used for modeling the problem. In order to provide the broader audience of researchers a better understanding of spreading patterns of epidemic diseases, a short introduction on biological systems modeling is also presented and the Matlab source codes for the simulations are provided online.

    Reza Sameni, “Mathematical Modeling of Epidemic Diseases; A Case Study of the COVID-19 Coronavirus,” arXiv:2003.11371 (2020).

  • Forecasting and Evaluating Intervention of COVID-19 in the World (Preprint)

    Zixin Hu et al. / March 22, 2020

    When the COVID-19 pandemic enters dangerous new phase, whether and when to take aggressive public health interventions to slow down the spread of COVID-19. To develop the artificial intelligence (AI) inspired methods for real-time forecasting and evaluating intervention strategies to curb the spread of COVID-19 in the World. A modified auto-encoder for modeling the transmission dynamics of the epidemics is developed and applied to the surveillance data of cumulative and new COVID-19 cases and deaths from WHO, as of March 16, 2020. The average errors of 5-step forecasting were 2.5%. The total peak number of cumulative cases and new cases, and the maximum number of cumulative cases in the world with later intervention (comprehensive public health intervention is implemented 4 weeks later) could reach 75,249,909, 10,086,085, and 255,392,154, respectively. The case ending time was January 10, 2021. However, the total peak number of cumulative cases and new cases and the maximum number of cumulative cases in the world with one week later intervention were reduced to 951,799, 108,853 and 1,530,276, respectively. Duration time of the COVID-19 spread would be reduced from 356 days to 232 days. The case ending time was September 8, 2020. We observed that delaying intervention for one month caused the maximum number of cumulative cases to increase 166.89 times, and the number of deaths increase from 53,560 to 8,938,725. We will face disastrous consequences if immediate action to intervene is not taken.

    Zixin Hu et al., “Forecasting and Evaluating Intervention of COVID-19 in the World,” arXiv:2003.09800 (2020).

  • The Early Phase of the COVID-19 Outbreak in Lombardy, Italy (Preprint)

    Danilo Cereda et al. / March 20, 2020

    In the night of February 20, 2020, the first case of novel coronavirus disease (COVID-19) was confirmed in the Lombardy Region, Italy. In the week that followed, Lombardy experienced a very rapid increase in the number of cases. We analyzed the first 5,830 laboratory-confirmed cases to provide the first epidemiological characterization of a COVID-19 outbreak in a Western Country. Epidemiological data were collected through standardized interviews of confirmed cases and their close contacts. We collected demographic backgrounds, dates of symptom onset, clinical features, respiratory tract specimen results, hospitalization, contact tracing. We provide estimates of the reproduction number and serial interval. The epidemic in Italy started much earlier than February 20, 2020. At the time of detection of the first COVID-19 case, the epidemic had already spread in most municipalities of Southern-Lombardy. The median age for of cases is 69 years (range, 1 month to 101 years). 47% of positive subjects were hospitalized. Among these, 18% required intensive care. The mean serial interval is estimated to be 6.6 days (95% CI, 0.7 to 19). We estimate the basic reproduction number at 3.1 (95% CI, 2.9 to 3.2). We estimated a decreasing trend in the net reproduction number starting around February 20, 2020. We did not observe significantly different viral loads in nasal swabs between symptomatic and asymptomatic. The transmission potential of COVID-19 is very high and the number of critical cases may become largely unsustainable for the healthcare system in a very short-time horizon. We observed a slight decrease of the reproduction number, possibly connected with an increased population awareness and early effect of interventions. Aggressive containment strategies are required to control COVID-19 spread and catastrophic outcomes for the healthcare system.

    Danilo Cereda et al., “The Early Phase of the COVID-19 Outbreak in Lombardy, Italy,” arXiv:2003.09320 (2020).

  • Modelling and Predicting the Spatio-Temporal Spread of Coronavirus Disease 2019 (COVID-19) in Italy (Preprint)

    Diego Giuliani et al. / March 20, 2020

    Official freely available data about the number of infected at the finest possible level of spatial areal aggregation (Italian provinces) are used to model the spatio-temporal distribution of COVID-19 infections at local level. Data time horizon ranges from 26 February 2020, which is the date when the first case not directly connected with China has been discovered in northern Italy, to 18 March 2020. An endemic-epidemic multivariate time-series mixed-effects generalized linear model for areal disease counts has been implemented to understand and predict spatio-temporal diffusion of the phenomenon. Previous literature has shown that these class of models provide reliable predictions of infectious diseases in time and space. Three subcomponents characterize the estimated model. The first is related to the evolution of the disease over time; the second is characterized by transmission of the illness among inhabitants of the same province; the third remarks the effects of spatial neighbourhood and try to capture the contagion effects of nearby areas. Focusing on the aggregated time-series of the daily counts in Italy, the contribution of any of the three subcomponents do not dominate on the others and our predictions are excellent for the whole country, with an error of 3 per thousand compared to the late available data. At local level, instead, interesting distinct patterns emerge. In particular, the provinces first concerned by containment measures are those that are not affected by the effects of spatial neighbours. On the other hand, for the provinces the are currently strongly affected by contagions, the component accounting for the spatial interaction with surrounding areas is prevalent. Moreover, the proposed model provides good forecasts of the number of infections at local level while controlling for delayed reporting.

    Diego Giuliani et al., “Modelling and Predicting the Spatio-Temporal Spread of Coronavirus Disease 2019 (COVID-19) in Italy,” arXiv:2003.06664 (2020).

  • Global Epidemiology of Coronavirus Disease 2019 (COVID-19): Disease Incidence, Daily Cumulative Index, Mortality, and Their Association with Country Healthcare Resources and Economic Status

    Chih-Cheng Lai et al. / March 19, 2020

    It has been 2 months since the first case of coronavirus disease 2019 (COVID-19) was reported in Wuhan. So far, COVID-19 has affected 84,503 patients in 57 countries/territories and caused 2,924 deaths in nine countries. However, the epidemiology data differ across countries. Although China had higher morbidity and mortality than other sites, the number of new cases per day in China is lesser than that outside of China since February 26, 2020. The incidence ranged from 61.4 per 1,000,000 people in Republic of Korea to 0.0002 per 1,000,000 people in India. The daily cumulative index (DCI) of COVID-19 (cumulative cases/no. of days between the first reported case and February 29, 2020) was greatest in China (1,320.85 per day), followed by Republic of Korea (78.78 per day), Iran (43.11 per day), and Italy (30.62 per day). However, the DCI in other countries/territories were less than 10 per day. Several effective measures including restricting travel from China, controlling the distribution of masks, extensive investigation of COVID-19 spread, and at once daily press conference by government to inform and educate people were aggressively conducted in Taiwan. This is probably the reason why there was only 39 cases (as of February 29, 2020) with a DCI of 1 case per day in Taiwan, which was much lower than that of nearby countries, such as Republic of Korea and Japan. Additionally, the incidence and mortality were correlated with DCI. However, further study and continued monitoring are needed to better understand the underlying mechanism of COVID-19.

    Chih-Cheng Lai et al., “Global Epidemiology of Coronavirus Disease 2019 (COVID-19): Disease Incidence, Daily Cumulative Index, Mortality, and Their Association with Country Healthcare Resources and Economic Status,” International Journal of Antimicrobial Agents, no. 105,946 (2020), doi:10.1016/j.ijantimicag.2020.105946.

  • Epidemiological and Clinical Characteristics of COVID-19 in Adolescents and Young Adults (Preprint)

    Jiaqiang Liao et al. / March 10, 2020

    Background: Adolescents and young adults might play a key role in the worldwide spread of Coronavirus Disease 2019 (COVID-19), because they are more likely to be involved in overseas studying, business, working, and traveling. However, the epidemiological and clinical characteristics of them are still unknown. Methods: We collected data of 46 confirmed COVID-19 patients aged 10 to 35 years from the Chongqing Three Gorges Central Hospital. The demographic, epidemiological, and clinical data were collected. Several key epidemiological parameters, the asymptomatic cases and transmission to their family members and the clinical characteristics at admission, and during treatment were summarized.
    Results: Of 46 confirmed patients, 14 patients (30.4%) were aged from 10 to 24 years, and 24 (52.2%) patients were male. The estimated mean incubation period was 6.6 days (95% confidence interval (CI) 4.4 - 9.6). The median serial interval was 1.9 days (95% CI 0.4 - 6.2). Three of the asymptomatic cases showed the transmission to their family members. Only 1 patient was identified as a severe case at admission. The common symptoms atadmission were dry cough (34, 81.0%), and fever (29, 69.1%). Nearly 60% of the patients showed ground-glass opacity by chest CT findings. Three patients developed acute kidney injury during treatment. Most of the patients (78.3%) were recovered and discharged by the end of the follow-up.
    Conclusions: This single center study with a relatively small sample size showed that the adolescent and young adult patients of COVID-19 had a long incubation period and a short serial interval. The transmission occurred from asymptomatic cases to their family. Fewer patients have developed complications during treatment.

    Jiaqiang Liao et al., “Epidemiological and Clinical Characteristics of COVID-19 in Adolescents and Young Adults,” medRxiv (2020), doi:10.1016/j.xinn.2020.04.001.

  • Epidemiological Analysis on a Family Cluster of COVID-19 (Preprint)

    Yuanying Qiu et al. / March 5, 2020

    Objectives
    To understand the possible transmission route of a family cluster of COVID-19 in Zhengzhou and the potential infectivity of COVID-19 in incubation period, and provide scientific evidence for the timely control of infectious source and curb the spread of the epidemic.
    Methods
    Epidemiological investigation was conducted for a family cluster of COVID-19 (8 cases) with descriptive epidemiological method, and respiratory tract samples of the cases were collected for the nucleic acid detection of 2019-nCoV by RT-PCR.
    Results
    Two primary cases, which occurred on 31 January and 1 February 2020, respectively, had a common exposure history in Wuhan. The other six family members had onsets on 30 January, 31 January, 1 February (three cases) and 3 February 2020.
    Conclusions
    In this family cluster of COVID-19, six family members were infected through common family exposure to the 2 primary cases. Five secondary cases had onsets earlier than or on the same day as the primary cases, indicating that COVID-19 is contagious in incubation period, and the home isolation in the early phase of the epidemic might lead to the risk of family cluster of COVID-19.

    Yuanying Qiu et al., “Epidemiological Analysis on a Family Cluster of COVID-19 [一起新型冠状病毒肺炎家庭聚集性疫情的流行病学分析],” Chinese Journal of Epidemiology (2020), doi:10.3760/cma.j.cn112338-20200221-00147.

  • Transmission Potential of the Novel Coronavirus (COVID-19) Onboard the Diamond Princess Cruises Ship, 2020

    Kenji Mizumoto and Gerardo Chowell / February 29, 2020

    An outbreak of COVID-19 developed aboard the Princess Cruises Ship during January–February 2020. Using mathematical modeling and time-series incidence data describing the trajectory of the outbreak among passengers and crew members, we characterize how the transmission potential varied over the course of the outbreak. Our estimate of the mean reproduction number in the confined setting reached values as high as ~11, which is higher than mean estimates reported from community-level transmission dynamics in China and Singapore (approximate range: 1.1–7). Our findings suggest that Rt decreased substantially compared to values during the early phase after the Japanese government implemented an enhanced quarantine control. Most recent estimates of Rt reached values largely below the epidemic threshold, indicating that a secondary outbreak of the novel coronavirus was unlikely to occur aboard the Diamond Princess Ship.

    Kenji Mizumoto and Gerardo Chowell, “Transmission Potential of the Novel Coronavirus (COVID-19) Onboard the Diamond Princess Cruises Ship, 2020,” Infectious Disease Modelling 5 (2020): 264–70, doi:10.1016/j.idm.2020.02.003.

  • A Mathematical Model for Simulating the Phase-Based Transmissibility of a Novel Coronavirus

    Tian-Mu Chen et al. / February 28, 2020

    Background
    As reported by the World Health Organization, a novel coronavirus (2019-nCoV) was identified as the causative virus of Wuhan pneumonia of unknown etiology by Chinese authorities on 7 January 2020. The virus was named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by International Committee on Taxonomy of Viruses on 11 February 2020. This study aimed to develop a mathematical model for calculating the transmissibility of the virus.
    Methods
    In this study, we developed a Bats-Hosts-Reservoir-People transmission network model for simulating the potential transmission from the infection source (probably be bats) to the human infection. Since the Bats-Hosts-Reservoir network was hard to explore clearly and public concerns were focusing on the transmission from Huanan Seafood Wholesale Market (reservoir) to people, we simplified the model as Reservoir-People (RP) transmission network model. The next generation matrix approach was adopted to calculate the basic reproduction number (R0) from the RP model to assess the transmissibility of the SARS-CoV-2.
    Results
    The value of R0 was estimated of 2.30 from reservoir to person and 3.58 from person to person which means that the expected number of secondary infections that result from introducing a single infected individual into an otherwise susceptible population was 3.58.
    Conclusions
    Our model showed that the transmissibility of SARS-CoV-2 was higher than the Middle East respiratory syndrome in the Middle East countries, similar to severe acute respiratory syndrome, but lower than MERS in the Republic of Korea.

    Tian-Mu Chen et al., “A Mathematical Model for Simulating the Phase-Based Transmissibility of a Novel Coronavirus,” Infectious Diseases of Poverty 9, no. 24 (2020), doi:10.1186/s40249-020-00640-3.

  • Contact Transmission of COVID-19 in South Korea: Novel Investigation Techniques for Tracing Contacts

    COVID-19 National Emergency Response Center, Epidemiology & Case Management Team, Korea Centers for Disease Control & Prevention / February 18, 2020

    In the epidemiological investigation of an infectious disease, investigating, classifying, tracking, and managing contacts by identifying the patient’s route are important for preventing further transmission of the disease. However, omissions and errors in previous activities can occur when the investigation is performed through only a proxy interview with the patient. To overcome these limitations, methods that can objectively verify the patient’s claims (medical facility records, Global Positioning System, card transactions, and closed-circuit television) were used for the recent ongoing coronavirus disease 2019 contact investigations in South Korea.

    COVID-19 National Emergency Response Center, Epidemiology & Case Management Team, Korea Centers for Disease Control & Prevention, “Contact Transmission of COVID-19 in South Korea: Novel Investigation Techniques for Tracing Contacts,” Osong Public Health and Research Perspectives 11, no. 1 (2020): 60–63, doi:10.24171/j.phrp.2020.11.1.09.

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