Forecasting the Effectiveness of Interventions to Control the Second Wave of COVID-19 Outbreaks: A Modelling Study in 12 Countries of 6 Regions (preprint)

Background: In most countries, it is hard to effectively control coronavirus disease 2019 (COVID-19). This study conducted the most comprehensive evaluation of the effects of pharmacological (like vaccination, pharmacotherapy ) and non-pharmacological (like isolation, social distancing and mask-wearing) interventions taken singly or in combination for the first time globally.Methods: We estimate that across these 12 countries that are different but presentative, interventions prevented or delayed roughly millions of confirmed cases. This study constructs mathematical model, which interventions includes vaccination, pharmacotherapy, isolation, social distancing and mask-wearing , and analyses the effect of these interventions used alone and in combination.Findings: The basic reproduction number (R0) of each country mostly range from 3 to 5. In terms of the effect of single intervention, for countries such as China, South Korea, Thailand, US, South Africa and Algeria, it is preferred to recommend these countries to adopt isolation to prevent and control the second wave of COVID-19 outbreak, while for countries such as Russia, UK, Saudi Arabia, India and Brazil, wearing masks is the best choice. Especially pharmacotherapy can play a good role in Iran. When combinations with different interventions were taken, the situation was different. For US, Brazil and Algeria, the combination of “Vaccination & Isolation & Wearing mask” is recommended in these countries to prevent and control the development of COVID-19, and the combination of “Isolation & Social distancing & Wearing mask” is recommended in UK and China. For the rest, we suggest that Russia, Iran, Saudi Arabia, India, Thailand and South Africa take the intervention measures of “Vaccination & Medical treatment & Isolation & Wearing mask”, “Vaccination & Medical treatment”, “Vaccination & Social distancing & Wearing mask”, “Medical treatment & Social distancing & Wearing mask”, “Vaccination & Medical Treatment & Isolation”, “Vaccination & Medical Treatment & Wearing mask”, respectively to deal with the second wave of outbreaks that may come by the end of this year.Interpretation: Our model is operable and selective for the prevention and control of epidemic situations in various countries. These findings may help policy makers in the 180+ countries where COVID-19 has been reported around the world to identify the most effective and socioeconomically acceptable measures to prevent and control the second wave of COVID-19 epidemic, and inform if when these policies should be deployed, intensified or replaced.Funding: This study was partly supported by the Bill & Melinda Gates Foundation (INV-005834), the Science and Technology Program of Fujian Province (No: 2020Y0002), the Xiamen New Coronavirus Prevention and Control Emergency Tackling Special Topic Program (No: 3502Z2020YJ03), and the Open Research Fund of State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics (SKLVD2019KF005).Declaration of Interests: The authors declare no competing interests.

Effectiveness of Pharmacological Interventions to Control COVID-19: An Age-Specific Modelling Study (preprint)

Background: Novel coronavirus disease 2019 (COVID-19) causes an immense disease burden. Only drugs or vaccines can eliminate the virus. Methods: We adopted our age-specific transmission model by susceptible-exposed-infectious -critically ill-asymptomatic-removed (SEICAR) model. Effects of different drug types were simulated by changing transmission rate (β), critical case fatality rate (fc), and disease duration of each age group. Evaluation indexes were based on outbreak duration(OD), cumulative number of cases(CNC), total attack rate(TAR), peak date(PD), number of peak cases(NPC), and case fatality rate(f). Findings: When without intervention, changing in β and disease duration, as the age increased, OD decreased, TAR increased, PD advanced, CCN and NPC initially increased and then decreased, while f decreased first and then increased. When disease duration and β remained unchanged, changing fc did not affect the epidemic. All age groups had 40% shorter disease duration but unchanged fc, while β was reduced by 60%, which reduced TAR of group 1 (≤14 years) from 2·35% to 0·09%; f of group 4 (≥65 years) was reduced from 1·04% to 0·05%. Interpretation: Drugs had different age-dependent effects. If a drug can control the disease duration or β of all age groups, younger people would have the fastest transmission control and seniors will have the best improvement in disease severity. Funding: The Bill & Melinda Gates Foundation (INV-005834); the Science and Technology Program of Fujian Province (No: 2020Y0002), and the Xiamen New Coronavirus Prevention and Control Emergency Tackling Special Topic Program (No: 3502Z2020YJ03).Declaration of Interests: The authors declare no competing interests.

Evaluating the effectiveness of countermeasures to control the novel coronavirus disease 2019 in Jilin Province, China (preprint)

Objective: Based on differences in populations and prevention and control measures, the spread of new coronary pneumonia in different countries and regions also differs. This study aimed to calculate the transmissibility of coronavirus disease 2019 (COVID-19), and to evaluate the effectiveness of countermeasures to control the disease in Jilin Province, China. Methods: : The data of reported COVID-19 cases were collected, including imported and local cases from Jilin Province as of March 14, 2019. A Susceptible–Exposed–Infectious–Asymptomatic–Recovered (SEIAR) model was developed to fit the data, and the effective reproduction number ( R eff ) was calculated at different stages in the province. Finally, the effectiveness of the countermeasures was assessed. Results: : A total of 97 COVID-19 infections were reported in Jilin Province, among which 45 were imported infections (including one asymptomatic infection) and 52 were local infections (including three asymptomatic infections). The model fit well with the reported data ( R 2 = 0.593, P < 0.001). The R eff of COVID-19 before and after February 1, 2020 was 1.64 and 0.05, respectively. Without the intervention taken on February 1, 2020, the predicted cases would reach a peak of 177,011 on October 22, 2020 (284 days from the first case). The projected number of cases until the end of the outbreak (on October 9, 2021) would be 17,129,367, with a total attack rate of 63.66%. Based on the comparison between the predicted incidence of the model and the actual incidence, the comprehensive intervention measures implemented in Jilin Province on February 1 reduced the incidence of cases by 99.99%. Therefore, according to the current measures and implementation efforts, Jilin Province can achieve good control of the virus’s spread. Conclusions: : COVID-19 has a moderate transmissibility in Jilin Province, China. The interventions implemented in the province had proved effective, increasing social distancing and a rapid response by the prevention and control system will help control the spread of the disease.

The relationship between key natural and social factors and the transmission of novel coronavirus disease 2019 in China (preprint)

Background Novel coronavirus disease 2019 (COVID-19) has become a global pandemic. This study aims to explore the relationship between key natural and social factors and the transmission of COVID-19 in China.Methods This study collected the number of confirmed cases of COVID-19 in 21 provinces and cities in China as of February 28, 2020. Three provinces were included in the sample: Hainan, Guizhou, and Qinghai. The 18 cities included Shanghai, Tianjin and so on. Key natural factors comprised monthly average temperatures in the January and February 2020 and spatial location as determined by longitude and latitude. Social factors were population density, Gross Domestic Product (GDP), number of medical institutions and health practitioners; as well as the per capita values for GDP, medical institutions, and health practitioners. Excel was used to collate the data and draw the temporal and spatial distribution map of the prevalence rate (PR) and the proportion of local infection (PLI). The influencing factors were analyzed by SPSS 21.0 statistical software, and the relationship between the dependent and independent variables was simulated by 11 models. Finally, we choose the exponential model according to the value of R2 and the applicability of the model.Results The temporal and spatial distribution of the PR varies across the 21 provinces and cities identified. The PR generally decreases with distance from Hubei, except in the case of Shenzhen City, where the converse is observed. The results of the exponential model simulation show that the monthly minimum, median, and maximum average temperatures in January and February, and the latitude and population density are significant and thus will affect the PLI. The corresponding values of R2 are 0.297, 0.322, 0.349, 0.290, 0.314, 0.339, 0.344, and 0.301. The effects of other factors were not statistically significant.Conclusions Among the selected key natural and social factors, higher temperatures may decrease the transmission of COVID-19. From this analysis, it is evident that if the temperature decreases by 1℃, the average PLI increases by 0.01. Further, it was established that locations at more northern latitudes had a higher PLI, and population density showed an inverse relationship with PLI.

A mathematical model for estimating the age-specific transmissibility of a novel coronavirus (preprint)

Background: A novel coronavirus named as "SARS-CoV-2" has spread widely in many countries since December 2019, especially in China. This study aimed to quantify the age-specific transmissibility by using a mathematical model. Methods: An age-specific susceptible - exposed - symptomatic - asymptomatic - recovered - seafood market (SEIARW) model was developed based on two suspected transmission routes (from market to person and person to person). The susceptible people from Wuhan City were divided into different age groups. We used the subscript i and j to represent age group 1 to 4 (1: <= 14 years; 2: 15-44 years; 3: 45-64 years; 4: >= 65 years) and 1 to 5 (1: <= 5 years; 2: 6-14 years; 3: 15-24 years; 4: 25-59 years; 4: >= 60 years), respectively. Data of reported COVID-19 cases were collected from one published literature from 26 November to 22 December, 2019 in Wuhan City, China. The age-specific transmissibility of the virus was estimated accordingly secondary attack rate (SAR). Results: The age-specific SEIARW model fitted with the reported data well by dividing the population into four age groups ({chi}2 = 4.99 x 10-6, P > 0.999), and five age groups ({chi}2 = 4.85 x 10-6, P > 0.999). Based on the four-age-group SEIARW model, the highest transmissibility occurred from age group 2 to 3 (SAR23 = 17.56 per 10 million persons), followed by from age group 3 to 2 (SAR32 = 10.17 per 10 million persons). The lowest transmissibility occurred from age group 1 to 2 (SAR12 = 0.002 per 10 million persons). Based on the five-age-group SEIARW model, the highest transmissibility occurred from age group 4 to 5 (SAR45 = 12.40 per 10 million persons), followed by from age group 5 to 4 (SAR54 = 6.61 per 10 million persons). The lowest transmissibility occurred from age group 3 to 4 (SAR34 = 0.0002 per 10 million persons). Conclusions: SARS-CoV-2 has high transmissibility among adults and elder people but low transmissibility among children and young people.