Cumulative Number Of Infections Research Articles

Model-based analysis of the incidence trends and transmission dynamics of COVID-19 associated with the Omicron variant in representative cities in China

BackgroundIn 2022, Omicron outbreaks occurred at multiple sites in China. It is of great importance to track the incidence trends and transmission dynamics of coronavirus disease 2019 (COVID-19) to guide further interventions.MethodsGiven the population size, economic level and transport level similarities, two groups of outbreaks (Shanghai vs. Chengdu and Sanya vs. Beihai) were selected for analysis. We developed the SEAIQRD, ARIMA, and LSTM models to seek optimal modeling techniques for waves associated with the Omicron variant regarding data predictive performance and mechanism transmission dynamics, respectively. In addition, we quantitatively modeled the impacts of different combinations of more stringent interventions on the course of the epidemic through scenario analyses.ResultsThe best-performing LSTM model showed better prediction accuracy than the best-performing SEAIQRD and ARIMA models in most cases studied. The SEAIQRD model had an absolute advantage in exploring the transmission dynamics of the outbreaks. Regardless of the time to inflection point or the time to Rt curve below 1.0, Shanghai was later than Chengdu (day 46 vs. day 12/day 54 vs. day 14), and Sanya was later than Beihai (day 16 vs. day 12/day 20 vs. day 16). Regardless of the number of peak cases or the cumulative number of infections, Shanghai was higher than Chengdu (34,350 vs. 188/623,870 vs. 2,181), and Sanya was higher than Beihai (1,105 vs. 203/16,289 vs. 3,184). Scenario analyses suggested that upgrading control level in advance, while increasing the index decline rate and quarantine rate, were of great significance for shortening the time to peak and Rt below 1.0, as well as reducing the number of peak cases and final affected population.ConclusionsThe LSTM model has great potential for predicting the prevalence of Omicron outbreaks, whereas the SEAIQRD model is highly effective in revealing their internal transmission mechanisms. We recommended the use of joint interventions to contain the spread of the virus.

Mathematical Modelling of Virus Spreading in COVID-19.

A mathematical model is proposed to analyze the spreading dynamics of COVID-19. By using the parameters of the model, namely the basic reproduction number (R0) and the attenuation constant (k), the daily number of infections (DNI) and the cumulative number of infections (CNI) over time (m) are deduced and shown to be in good agreement with experimental data. This model effectively addresses three key issues: (1) inferring the conditions under which virus infections die out for a specific strain given R0; (2) explaining the occurrence of second waves of infection and developing preventive measures; and (3) understanding the competitive spread of two viruses within a region and devising control strategies. The findings highlight the potential of this simple mathematical framework in comprehensively addressing these challenges. The theoretical insights derived from this model can guide the evaluation of infection wave severity and the formulation of effective strategies for controlling and mitigating epidemic outbreaks.

A mathematical prediction model of infectious diseases considering vaccine and temperature, and its prediction in Hong Kong

Since 2020, COVID-19 has launched a rather difficult challenge in public health all over the world. For the current situation of COVID-19 outbreaks in various places, predicting the trend of COVID-19 plays a vital role in later policy-making, allocating medical materials, and developing the economy and society. Although there may be corresponding specific drugs soon, which can reduce the mortality of COVID-19 to a relatively low level, but the main popular Omicron BA.1 and BA.2 strain is highly infectious, which has a great impact on the elderly population, so COVID-19 cannot be equated with general influenza. This paper aims to predict the trend of the number of infections over time and the final number of infections from the fifth wave of epidemic in Hong Kong. To establish a more practical infection model, this paper introduces an infectious disease transmission model with the influence of temperature and vaccine. The model shows that the fifth wave of the epidemic in Hong Kong will end at the end of April 2022. At that time, the cumulative number of infections is expected to reach about 1.6 million. By formulating and implementing reasonable policies, the final number of infections can be controlled at about 1.1 million. Therefore, we hope that the policymakers and managers of COVID-19 in Hong Kong will formulate and adopt reasonable measures to control these epidemic.

Should we allocate more COVID-19 vaccine doses to non-vaccinated individuals?

Following the approval by the FDA of two COVID-19 vaccines, which are administered in two doses three to four weeks apart, we simulate the effects of various vaccine distribution policies on the cumulative number of infections and deaths in the United States in the presence of shocks to the supply of vaccines. Our forecasts suggest that allocating more than 50% of available doses to individuals who have not received their first dose can significantly increase the number of lives saved and significantly reduce the number of COVID-19 infections. We find that a 50% allocation saves on average 33% more lives, and prevents on average 32% more infections relative to a policy that guarantees a second dose within the recommended time frame to all individuals who have already received their first dose. In fact, in the presence of supply shocks, we find that the former policy would save on average 8, 793 lives and prevents on average 607, 100 infections while the latter policy would save on average 6, 609 lives and prevents on average 460, 743 infections.

Testing and vaccination to reduce the impact of COVID-19 in nursing homes: an agent-based approach

BackgroundEfforts to protect residents in nursing homes involve non-pharmaceutical interventions, testing, and vaccine. We sought to quantify the effect of testing and vaccine strategies on the attack rate, length of the epidemic, and hospitalization.MethodsWe developed an agent-based model to simulate the dynamics of SARS-CoV-2 transmission among resident and staff agents in a nursing home. Interactions between 172 residents and 170 staff based on data from a nursing home in Los Angeles, CA. Scenarios were simulated assuming different levels of non-pharmaceutical interventions, testing frequencies, and vaccine efficacy to reduce transmission.ResultsUnder the hypothetical scenario of widespread SARS-CoV-2 in the community, 3-day testing frequency minimized the attack rate and the time to eradicate an outbreak. Prioritization of vaccine among staff or staff and residents minimized the cumulative number of infections and hospitalization, particularly in the scenario of high probability of an introduction. Reducing the probability of a viral introduction eased the demand on testing and vaccination rate to decrease infections and hospitalizations.ConclusionsImproving frequency of testing from 7-days to 3-days minimized the number of infections and hospitalizations, despite widespread community transmission. Vaccine prioritization of staff provides the best protection strategy when the risk of viral introduction is high.

The impacts of gradually terminating nonpharmaceutical interventions for SARS-CoV-2: A mathematical modelling analysis

With the expansion of vaccination programs, the policy of terminating nonpharmaceutical interventions for preventing the SARS-CoV-2 pandemic should become more flexible. The current study investigated the clinical and economic outcomes of intervention policies combining nonpharmaceutical interventions and vaccination programs for dealing with the SARS-CoV-2 pandemic. An agent-based transmission model was adopted that describes how a SARS-CoV-2 virus spreads in the populations of China. The model inputs were derived from the literature and expert opinion. The following intervention policies were simulated: no intervention, strict nonpharmaceutical interventions, and nonpharmaceutical interventions for workplace, community, school and home gradually terminated by combining vaccination programs for specified age groups (vaccination age in years: 20–60, 20–70, 20–80, ≥ 20, ≥ 10 and whole population). Cumulative infections and deaths in one calendar year, costs and quality-adjusted life years (QALYs) were measured. When the vaccination program was taken up in at least the ≥ 20 years age group in all populations, nonpharmaceutical interventions for workplace and community settings could be gradually terminated because the cumulative number of infections was < 100 per 100,000 persons. Further ending nonpharmaceutical interventions in school and home settings could not meet the target even when the vaccination program had been taken up in all populations. When cumulative deaths were used as the endpoint, nonpharmaceutical interventions in workplace, community and school settings could be gradually terminated. Vaccine efficacy and coverage have substantial impacts. Terminating nonpharmaceutical interventions in workplace settings could produce the lowest cost when vaccination programs are taken up at least in the ≥ 10 years age group; this method dominates most intervention strategies due to its lower costs and higher QALYs. According to our findings, nonpharmaceutical interventions might be gradually terminated in Chinese settings.

Quantitative Analysis of COVID-19 Pandemic Responses Based on an Improved SEIR-SD Model

In late 2019, the COVID-19 pandemic began to spread over the world, causing millions of deaths. In the first few months of the pandemic, several countries (such as China) prevented the spread of the pandemic successfully. By contrast, the pandemic in many other countries was not controlled well. For example, India encountered a second serious outbreak of COVID-19 from April 2021 due to the poor resistance measures implemented by the government. To figure out the effective countermeasures to the pandemic, this research proposes a COVID-19 pandemic and its response system, which consists of the infection subsystem, the quarantine subsystem, and the medical subsystem. On this basis, an improved SEIR-SD model is established which is utilized to analyze the response measures to the pandemic quantitatively. This model successfully simulates the actual epidemic scenarios in Wuhan, which verifies its effectiveness. Afterward, the impact of hospital administration rate, quarantine rate, average contact number, and contact infection rate on the cumulative number of infections and deaths are analyzed by simulation. The results show that both the medical and administrative efforts, especially in the early stage of the epidemic, are significant in reducing the number of infections and shortening the epidemic period. In the medical aspect, the more stringent quarantine brings the earlier inflection point of the epidemic; more importantly, improving the treatment rate significantly reduces the scale of the epidemic. In the administrative aspect, enforcing individual protection and strict community closure can effectively cut off the transmission of the virus and curb the spread of the epidemic. Finally, this research proposes several practical suggestions in response to the COVID-19 pandemic. The main contribution of this research is that the effects of different response measures on the number of new infections daily and the cumulative number of deaths of a country or region in the COVID-19 pandemic are estimated quantitatively based on modeling and simulation.

SARS-CoV-2 transmission dynamics in Belarus in 2020 revealed by genomic and incidence data analysis

BackgroundNon-pharmaceutical interventions (NPIs) have been implemented worldwide to curb COVID-19 spread. Belarus is a rare case of a country with a relatively modern healthcare system, where highly limited NPIs have been enacted. Thus, investigation of Belarusian COVID-19 dynamics is essential for the local and global assessment of the impact of NPI strategies.MethodsWe integrate genomic epidemiology and surveillance methods to investigate the spread of SARS-CoV-2 in Belarus in 2020. We utilize phylodynamics, phylogeography, and probabilistic bias inference to study the virus import and export routes, the dynamics of the effective reproduction number, and the incidence of SARS-CoV-2 infection.ResultsHere we show that the estimated cumulative number of infections by June 2020 exceeds the confirmed case number by a factor of ~4 (95% confidence interval (2; 9)). Intra-country SARS-CoV-2 genomic diversity originates from at least 18 introductions from different regions, with a high proportion of regional transmissions. Phylodynamic analysis indicates a moderate reduction of the effective reproductive number after the introduction of limited NPIs, but its magnitude is lower than for developed countries with large-scale NPIs. On the other hand, the effective reproduction number estimate is comparable with that for the neighboring Ukraine, where NPIs were broader.ConclusionsThe example of Belarus demonstrates how countries with relatively low outward population mobility continue to be integral parts of the global epidemiological environment. Comparison of the effective reproduction number dynamics for Belarus and other countries reveals the effect of different NPI strategies but also emphasizes the role of regional Eastern European sociodemographic factors in the virus spread.

An AHP-based regional COVID-19 vulnerability model and its application in China.

Since the COVID-19 outbreak, four cities—Wuhan, Beijing, Urumqi and Dalian—have experienced the process from outbreak to stabilization. According to the China Statistical Yearbook and China Center for Disease Control records, regional, pathological, medical and response attributes were selected as regional vulnerability factors of infectious diseases. Then the Analytic Hierarchy Process (AHP) method was used to build a regional vulnerability index model for the infectious disease. The influence of the COVID-19 outbreak at a certain place was assessed computationally in terms of the number of days of epidemic duration and cumulative number of infections, and then fitted to the city data. The resulting correlation coefficient was 0.999952. The range of the regional vulnerability index for COVID-19 virus was from 0.0513 to 0.9379. The vulnerability indexes of Wuhan, Urumqi, Beijing and Dalian were 0.8733, 0.1951, 0.1566 and 0.1119, respectively. The lack of understanding of the virus became the biggest breakthrough point for the rapid spread of the virus in Wuhan. Due to inadequate prevention and control measures, the city of Urumqi was unable to trace the source of infection and close contacts, resulting in a relatively large impact. Beijing has both high population density and migration rate, which imply that the disease outbreak in this city had a great impact. Dalian has perfect prevention and good regional attributes. In addition, the regional vulnerability index model was used to analyze other Chinese cities. Accordingly, the regional vulnerability index and the prevention and control suggestions for them were discussed.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40808-021-01244-y.

Environmental and climatic impact on the infection and mortality of SARS-CoV-2 in Peru.

The role of the environment and climate in the transmission and case fatality rates of SARS-CoV-2 is still being investigated a year into the pandemic. Elevation and air quality are believed to be significant factors in the development of the pandemic, but the influence of additional environmental factors remains unclear. We explored the relationship between the cumulative number of infections and mortality cases with climate (temperature, precipitation, solar radiation, water vapor pressure, wind), environmental data (elevation, normalized difference vegetation index or NDVI, particulate matter at 2.5 μm or PM2.5 and NO2 concentration), and population density in Peru. We use confirmed cases of infection from 1,287 districts and mortality in 479 districts, we used Spearman's correlations to assess the bivariate correlation between environmental and climatic factors with cumulative infection cases, cumulative mortality and case-fatality rate. We explored district cases within the ecozones of coast, sierra, high montane forest and lowland rainforest. Multiple linear regression models indicate elevation, mean solar radiation, air quality, population density and green vegetation cover, as a socioeconomic proxy, are influential factors in the distribution of infection and mortality of SARS-CoV-2 in Peru. Case-fatality rate was weakly associated with elevation. Our results also strongly suggest that exposure to poor air quality is a significant factor in the mortality of individuals below the age of 30. We conclude that environmental and climatic factors do play a significant role in the transmission and case fatality rates in Peru, however further study is required to see if these relationships are maintained over time.

Impact of contact tracing, respect of isolation, and lockdown in reducing the number of cases infected with COVID-19. Case study: Tunisia’s response from March 22 to May 4, 2020

BackgroundCoronavirus disease 2019 (COVID-19) has spread rapidly across the world. Tunisia reacted early to COVID-19, resulting in a low number of infections during the first wave of the pandemic. This study was performed to model the effects of different interventions on the evolution of cases and to compare these with the Tunisian experience. MethodsA stochastic transmission model was used to quantify the reduction in number of cases of COVID-19 with the interventions of contact tracing, compliance with isolation, and a general lockdown. ResultsIn the model, increasing contact tracing from 20% to 80% after the first 100 cases reduced the cumulative number of infections (CNI) by 52% in 1 month. Similarly, increased compliance with isolation from 20% to 80% after the first 100 cases reduced the CNI by 45%. These reductions were smaller if the interventions were implemented after 1000 cases. A general lockdown reduced the CNI by 97% after the first 100 cases. Tunisia implemented its general lockdown after 75 cases were confirmed, which reduced the cumulative number of infected cases by 86% among the general population. ConclusionsThis study shows that the early application of critical interventions contributes significantly to reducing infections and the evolution of COVID-19 in a country. Tunisia’s early success with the control of COVID-19 is explained by its quick response.

How social capital helps communities weather the COVID-19 pandemic.

Why have the effects of COVID-19 been so unevenly geographically distributed in the United States? This paper investigates the role of social capital as a mediating factor for the spread of the virus. Because social capital is associated with greater trust and relationships within a community, it could endow individuals with a greater concern for others, thereby leading to more hygienic practices and social distancing. Using data for over 2,700 US counties, we investigate how social capital explains the level and growth rate of infections. We find that moving a county from the 25th to the 75th percentile of the distribution of social capital would lead to a 18% and 5.7% decline in the cumulative number of infections and deaths, as well as suggestive evidence of a lower spread of the virus. Our results are robust to many demographic characteristics, controls, and alternative measures of social capital.

CSF3 Is a Potential Drug Target for the Treatment of COVID-19.

Coronavirus Disease 2019 (COVID-19) is an acute respiratory infectious disease that appeared at the end of 2019. As of July 2020, the cumulative number of infections and deaths have exceeded 15 million and 630,000, respectively. And new cases are increasing. There are still many difficulties surrounding research on the mechanism and development of therapeutic vaccines. It is urgent to explore the pathogenic mechanism of viruses to help prevent and treat COVID-19. In our study, we downloaded two datasets related to COVID-19 (GSE150819 and GSE147507). By analyzing the high-throughput expression matrix of uninfected human bronchial organoids and infected human bronchial organoids in the GSE150819, 456 differentially expressed genes (DEGs) were identified, which were mainly enriched in the cytokine–cytokine receptor interaction pathway and so on. We also constructed the protein–protein interaction (PPI) network of DEGs to identify the hub genes. Then we analyzed GSE147507, which contained lung adenocarcinoma cell lines (A549 and Calu3) and the primary bronchial epithelial cell line (NHBE), obtaining 799, 460, and 46 DEGs, respectively. The results showed that in human bronchial organoids, A549, Calu3, and NHBE samples infected with SARS-CoV-2, only one upregulated gene CSF3 was identified. Interestingly, CSF3 is one of the hub genes we previously screened in GSE150819, suggesting that CSF3 may be a potential drug target. Further, we screened potential drugs targeting CSF3 by MOE; the top 50 drugs were screened by flexible docking and rigid docking, with 37 intersections. Two antiviral drugs (Elbasvir and Ritonavir) were included; Elbasvir and Ritonavir formed van der Waals (VDW) interactions with surrounding residues to bind with CSF3, and Elbasvir and Ritonavir significantly inhibited CSF3 protein expression.

Infectious Disease Burden and the Risk of Alzheimer's Disease: A Population-Based Study.

Previous studies suggested a link between various infectious pathogens and the development of Alzheimer's disease (AD), posing the question whether infectious disease could present a novel modifiable risk factor. To assess whether infectious disease burden due to clinically apparent infections is associated with an increased risk of AD. We conducted a population-based nested case-control study using the United Kingdom Clinical Practice Research Datalink. We included all dementia-free subjects ≥50 years of age enrolling in the database between January 1988 and December 2017. Each case of AD identified during follow-up was matched with up to 40 controls. Conditional logistic regression estimated adjusted odds ratios (ORs) with 95% confidence intervals (CIs) of AD associated with ≥1 infection diagnosed > 2 years before the index date compared with no infection during the study period. We further stratified by time since first infection and cumulative number of infections. The cohort included overall 4,262,092 individuals (mean age at cohort entry 60.4 years; 52% female). During a median follow-up of 10.5 years, 40,455 cases of AD were matched to 1,610,502 controls. Compared with having no burden of infectious disease, having a burden of infectious disease was associated with an increase in the risk of AD (OR, 1.05; 95% CI, 1.02 to 1.08). The risk increased with longer time since first infection, peaking after 12-30 years (OR, 1.11; 95% CI, 1.05-1.17). The risk did not increase with cumulative number of infections. The overall risk of AD associated with infectious disease burden was small but increased gradually with longer time since first infection.

The Clinical Impact and Cost Effectiveness of Intervention Policies Against the SARS-CoV-2 Pandemic: A Mathematical Modelling Analysis

Background: To investigate the clinical and economic outcomes, of intervention policies combining nonpharmaceutical interventions and vaccination programs for dealing with the SARS-CoV-2 pandemic. Methods: An agent-based model transmission model was adopted which describes how a SARS-CoV-2 virus will spread in the populations of China. The model inputs were derived from literature and expert opinion. The following intervention policies were simulated: no intervention, strict nonpharmaceutical interventions, nonpharmaceutical intervention for workplace, community, school and home gradually dismissed by combining vaccination programs in pre-specified age groups. The cumulative infection and death in one calendar year, costs and quality adjusted life years (QALYs) were measured. Findings: When the vaccination program had been at least taken up in ≥20 years and all populations, nonpharmaceutical intervention for workplace and community settings could be gradually dismissed because the cumulative number of infections was < 100 per 100,000 persons. Further dismissing nonpharmaceutical intervention for school and home settings could not meet the target even when the vaccination program had been taken up in all populations. When cumulative death was used as the endpoint, nonpharmaceutical intervention in workplace, community and school settings could be gradually dismissed. Vaccine efficacy and coverage have substantial impacts. Dismissing nonpharmaceutical interventions in workplace settings could produce the lowest cost when vaccination programs are at least taken up at age ≥10 years, which also dominates most intervention strategies due to its lower costs and higher QALYs. Interpretation: Continued nonpharmaceutical interventions might be gradually dismissed until herd immunity is achieved. Funding: None to declare. Declaration of Interest: None to declare.

Robust estimates of the true (population) infection rate for COVID-19: a backcasting approach.

Differences in COVID-19 testing and tracing across countries, as well as changes in testing within each country over time, make it difficult to estimate the true (population) infection rate based on the confirmed number of cases obtained through RNA viral testing. We applied a backcasting approach to estimate a distribution for the true (population) cumulative number of infections (infected and recovered) for 15 developed countries. Our sample comprised countries with similar levels of medical care and with populations that have similar age distributions. Monte Carlo methods were used to robustly sample parameter uncertainty. We found a strong and statistically significant negative relationship between the proportion of the population who test positive and the implied true detection rate. Despite an overall improvement in detection rates as the pandemic has progressed, our estimates showed that, as at 31 August 2020, the true number of people to have been infected across our sample of 15 countries was 6.2 (95% CI: 4.3–10.9) times greater than the reported number of cases. In individual countries, the true number of cases exceeded the reported figure by factors that range from 2.6 (95% CI: 1.8–4.5) for South Korea to 17.5 (95% CI: 12.2–30.7) for Italy.

Phenomenological Modelling of COVID-19 Epidemics in Sri Lanka, Italy, the United States, and Hebei Province of China.

The COVID-19 pandemic has resulted in increasing number of infections and deaths every day. Lack of specialized treatments for the disease demands preventive measures based on statistical/mathematical models. The analysis of epidemiological curve fitting, on number of daily infections across affected countries, provides useful insights on the characteristics of the epidemic. A variety of phenomenological models are available to capture the dynamics of disease spread and growth. The number of daily new infections and cumulative number of infections in COVID-19 over four selected countries, namely, Sri Lanka, Italy, the United States, and Hebei province of China, from the first day of appearance of cases to 2nd July 2020 were used in the study. Gompertz, logistic, Weibull, and exponential growth curves were fitted on the cumulative number of infections across countries. AIC, BIC, RMSE, and R2 were used to determine the best fitting curve for each country. Results revealed that the most appropriate growth curves for Sri Lanka, Italy, the United States, and China (Hebei) are the logistic, Gompertz, Weibull, and Gompertz curves, respectively. Country-wise, overall growth rate, final epidemic size, and short-term forecasts were evaluated using the selected model. Daily log incidences in each country were regressed before and after the identified peak time of the respective outbreak of epidemic. Hence, doubling time/halving time together with daily growth rates and predictions was estimated. Findings and relevant interpretations demonstrate that the outbreak seems to be extinct in Hebei, China, whereas further transmissions are possible in the United States. In Italy and Sri Lanka, current outbreaks transmit in a decreasing rate.

Infection as a Stroke Risk Factor and Determinant of Outcome After Stroke.

Understanding the relationship between infection and stroke has taken on new urgency in the era of the coronavirus disease 2019 (COVID-19) pandemic. This association is not a new concept, as several infections have long been recognized to contribute to stroke risk. The association of infection and stroke is also bidirectional. Although infection can lead to stroke, stroke also induces immune suppression which increases risk of infection. Apart from their short-term effects, emerging evidence suggests that poststroke immune changes may also adversely affect long-term cognitive outcomes in patients with stroke, increasing the risk of poststroke neurodegeneration and dementia. Infections at the time of stroke may also increase immune dysregulation after the stroke, further exacerbating the risk of cognitive decline. This review will cover the role of acute infections, including respiratory infections such as COVID-19, as a trigger for stroke; the role of infectious burden, or the cumulative number of infections throughout life, as a contributor to long-term risk of atherosclerotic disease and stroke; immune dysregulation after stroke and its effect on the risk of stroke-associated infection; and the impact of infection at the time of a stroke on the immune reaction to brain injury and subsequent long-term cognitive and functional outcomes. Finally, we will present a model to conceptualize the many relationships among chronic and acute infections and their short- and long-term neurological consequences. This model will suggest several directions for future research.

Ties That Bind (and Social Distance): How Social Capital Helps Communities Weather the COVID-19 Pandemic

Why have the effects of COVID-19 been so unevenly geographically distributed in the United States? This paper investigates the role of social capital as a mediating factor for the spread of the virus. On one hand, higher social capital could imply greater in-person interaction and risk of contagion. On the other hand, because social capital is associated with greater trust and relationships within a community, it could endow individuals with a greater concern for others, thereby leading to more hygienic practices and social distancing. Using data for over 2,700 US counties, we investigate how social capital explains the level and growth rate of infections. We find that moving a county from the 25th to the 75th percentile of the distribution of social capital would lead to a 18% and 5.7% decline in the cumulative number of infections and deaths, as well as suggestive evidence of a lower spread of the virus. Our results are robust to many demographic characteristics, controls, and alternative measures of social capital. Finally, we discuss the channels through which social capital makes communities better able to respond to outbreaks.

Forecasting the 2019-ncov Epidemic in Wuhan by SEIR and Cellular Automata Model

Since December 2019, Wuhan has became an outbreak epicentre of the novel coronavirus pneumonia. This study inferred the epidemic spread in Wuhan, which is of great importance for government immediately deployed mitigation interventions. In view of the fact that the virus is infectious during the incubation period, we added new variables to the SEIR model. We used the data published by Wuhan government from Dec 8, 2019, to Feb 4, 2020 to forecast the spreading trend and the number of infected people, then simulated the results of different government control efforts by changing the control variables. Furthermore, we used mathematical formulas to infer the basic reproduction number, and then calculated the probability of the neighbor being infected at each time step, simulated the epidemic course of epidemic by using the cellular automata model. Our finding suggested that the epidemic outbreak earlier under the government’s powerful control measures and the outbreak period begin on mid-January, then reach a peak on the mid-February. The cumulative number of infections in Wuhan would be controlled at 58310. More importantly, The basic reproduction number is 3.13, which indicated that the virus is high risk. Simultaneously, the cellular automata model is very suitable for simulating the spread course after the public health interventions.