A Simulation on Potential Secondary Spread of Novel Coronavirus in an Exported Country Using a Stochastic Epidemic SEIR Model.
ABSTRACT: Ongoing outbreak of pneumonia caused by novel coronavirus (2019-nCoV) began in December 2019 in Wuhan, China, and the number of new patients continues to increase. Even though it began to spread to many other parts of the world, such as other Asian countries, the Americas, Europe, and the Middle East, the impact of secondary outbreaks caused by exported cases outside China remains unclear. We conducted simulations to estimate the impact of potential secondary outbreaks in a community outside China. Simulations using stochastic SEIR model were conducted, assuming one patient was imported to a community. Among 45 possible scenarios we prepared, the worst scenario resulted in the total number of persons recovered or removed to be 997 (95% CrI 990-1000) at day 100 and a maximum number of symptomatic infectious patients per day of 335 (95% CrI 232-478). Calculated mean basic reproductive number (R0) was 6.5 (Interquartile range, IQR 5.6-7.2). However, better case scenarios with different parameters led to no secondary cases. Altering parameters, especially time to hospital visit. could change the impact of a secondary outbreak. With these multiple scenarios with different parameters, healthcare professionals might be able to better prepare for this viral infection.
Project description:<h4>Background</h4>Since Dec 31, 2019, the Chinese city of Wuhan has reported an outbreak of atypical pneumonia caused by the 2019 novel coronavirus (2019-nCoV). Cases have been exported to other Chinese cities, as well as internationally, threatening to trigger a global outbreak. Here, we provide an estimate of the size of the epidemic in Wuhan on the basis of the number of cases exported from Wuhan to cities outside mainland China and forecast the extent of the domestic and global public health risks of epidemics, accounting for social and non-pharmaceutical prevention interventions.<h4>Methods</h4>We used data from Dec 31, 2019, to Jan 28, 2020, on the number of cases exported from Wuhan internationally (known days of symptom onset from Dec 25, 2019, to Jan 19, 2020) to infer the number of infections in Wuhan from Dec 1, 2019, to Jan 25, 2020. Cases exported domestically were then estimated. We forecasted the national and global spread of 2019-nCoV, accounting for the effect of the metropolitan-wide quarantine of Wuhan and surrounding cities, which began Jan 23-24, 2020. We used data on monthly flight bookings from the Official Aviation Guide and data on human mobility across more than 300 prefecture-level cities in mainland China from the Tencent database. Data on confirmed cases were obtained from the reports published by the Chinese Center for Disease Control and Prevention. Serial interval estimates were based on previous studies of severe acute respiratory syndrome coronavirus (SARS-CoV). A susceptible-exposed-infectious-recovered metapopulation model was used to simulate the epidemics across all major cities in China. The basic reproductive number was estimated using Markov Chain Monte Carlo methods and presented using the resulting posterior mean and 95% credibile interval (CrI).<h4>Findings</h4>In our baseline scenario, we estimated that the basic reproductive number for 2019-nCoV was 2·68 (95% CrI 2·47-2·86) and that 75 815 individuals (95% CrI 37 304-130 330) have been infected in Wuhan as of Jan 25, 2020. The epidemic doubling time was 6·4 days (95% CrI 5·8-7·1). We estimated that in the baseline scenario, Chongqing, Beijing, Shanghai, Guangzhou, and Shenzhen had imported 461 (95% CrI 227-805), 113 (57-193), 98 (49-168), 111 (56-191), and 80 (40-139) infections from Wuhan, respectively. If the transmissibility of 2019-nCoV were similar everywhere domestically and over time, we inferred that epidemics are already growing exponentially in multiple major cities of China with a lag time behind the Wuhan outbreak of about 1-2 weeks.<h4>Interpretation</h4>Given that 2019-nCoV is no longer contained within Wuhan, other major Chinese cities are probably sustaining localised outbreaks. Large cities overseas with close transport links to China could also become outbreak epicentres, unless substantial public health interventions at both the population and personal levels are implemented immediately. Independent self-sustaining outbreaks in major cities globally could become inevitable because of substantial exportation of presymptomatic cases and in the absence of large-scale public health interventions. Preparedness plans and mitigation interventions should be readied for quick deployment globally.<h4>Funding</h4>Health and Medical Research Fund (Hong Kong, China).
Project description:Background: The current novel coronavirus outbreak appears to have originated from a point-source exposure event at Huanan seafood wholesale market in Wuhan, China. There is still uncertainty around the scale and duration of this exposure event. This has implications for the estimated transmissibility of the coronavirus and as such, these potential scenarios should be explored. Methods: We used a stochastic branching process model, parameterised with available data where possible and otherwise informed by the 2002-2003 Severe Acute Respiratory Syndrome (SARS) outbreak, to simulate the Wuhan outbreak. We evaluated scenarios for the following parameters: the size, and duration of the initial transmission event, the serial interval, and the reproduction number (R0). We restricted model simulations based on the number of observed cases on the 25th of January, accepting samples that were within a 5% interval on either side of this estimate. Results: Using a pre-intervention SARS-like serial interval suggested a larger initial transmission event and a higher R0 estimate. Using a SARs-like serial interval we found that the most likely scenario produced an R0 estimate between 2-2.7 (90% credible interval (CrI)). A pre-intervention SARS-like serial interval resulted in an R0 estimate between 2-3 (90% CrI). There were other plausible scenarios with smaller events sizes and longer duration that had comparable R0 estimates. There were very few simulations that were able to reproduce the observed data when R0 was less than 1. Conclusions: Our results indicate that an R0 of less than 1 was highly unlikely unless the size of the initial exposure event was much greater than currently reported. We found that R0 estimates were comparable across scenarios with decreasing event size and increasing duration. Scenarios with a pre-intervention SARS-like serial interval resulted in a higher R0 and were equally plausible to scenarios with SARs-like serial intervals.
Project description:In December 2019, an outbreak of pneumonia caused by a novel coronavirus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) began in Wuhan, China. SARS-CoV-2 exhibited efficient person-to-person transmission of what became labeled as COVID-19. It has spread worldwide with over 83,000,000 infected cases and more than 1,800,000 deaths to date (December 31, 2020). This research proposes a statistical monitoring scheme in which an optimized np control chart is utilized by sentinel metropolitan airports worldwide for early detection of coronavirus and other respiratory virus outbreaks. The sample size of this chart is optimized to ensure the best overall performance for detecting a wide range of shifts in the infection rate, based on the available resources, such as the inspection rate and the allowable false alarm rate. The effectiveness of the proposed optimized np chart is compared with that of the traditional np chart with a predetermined sample size under both sampling inspection and 100% inspection. For a variety of scenarios including a real case, the optimized np control chart is found to substantially outperform its traditional counterpart in terms of the average number of infections. Therefore, this control chart has potential to be an effective tool for early detection of respiratory virus outbreaks, promoting early outbreak investigation and mitigation.
Project description:BackgroundEstimating key infectious disease parameters from the coronavirus disease (COVID-19) outbreak is essential for modelling studies and guiding intervention strategies.AimWe estimate the generation interval, serial interval, proportion of pre-symptomatic transmission and effective reproduction number of COVID-19. We illustrate that reproduction numbers calculated based on serial interval estimates can be biased.MethodsWe used outbreak data from clusters in Singapore and Tianjin, China to estimate the generation interval from symptom onset data while acknowledging uncertainty about the incubation period distribution and the underlying transmission network. From those estimates, we obtained the serial interval, proportions of pre-symptomatic transmission and reproduction numbers.ResultsThe mean generation interval was 5.20 days (95% credible interval (CrI): 3.78-6.78) for Singapore and 3.95 days (95% CrI: 3.01-4.91) for Tianjin. The proportion of pre-symptomatic transmission was 48% (95% CrI: 32-67) for Singapore and 62% (95% CrI: 50-76) for Tianjin. Reproduction number estimates based on the generation interval distribution were slightly higher than those based on the serial interval distribution. Sensitivity analyses showed that estimating these quantities from outbreak data requires detailed contact tracing information.ConclusionHigh estimates of the proportion of pre-symptomatic transmission imply that case finding and contact tracing need to be supplemented by physical distancing measures in order to control the COVID-19 outbreak. Notably, quarantine and other containment measures were already in place at the time of data collection, which may inflate the proportion of infections from pre-symptomatic individuals.
Project description:Dynamic SEIR (Susceptible, Exposed, Infectious, Removed) compartmental models provide a tool for predicting the size and duration of both unfettered and managed outbreaks-the latter in the context of interventions such as case detection, patient isolation, vaccination and treatment. The reliability of this tool depends on the validity of key assumptions that include homogeneity of individuals and spatio-temporal homogeneity. Although the SEIR compartmental framework can easily be extended to include demographic (e.g. age) and additional disease (e.g. healthcare workers) classes, dependence of transmission rates on time, and metapopulation structure, fitting such extended models is hampered by both a proliferation of free parameters and insufficient or inappropriate data. This raises the question of how effective a tool the basic SEIR framework may actually be. We go some way here to answering this question in the context of the 2014-2015 outbreak of Ebola in West Africa by comparing fits of an SEIR time-dependent transmission model to both country- and district-level weekly incidence data. Our novel approach in estimating the effective-size-of-the-populations-at-risk ( Neff) and initial number of exposed individuals ( E0) at both district and country levels, as well as the transmission function parameters, including a time-to-halving-the-force-of-infection ( tf/2) parameter, provides new insights into this Ebola outbreak. It reveals that the estimate R0 ? 1.7 from country-level data appears to seriously underestimate R0 ? 3.3 - 4.3 obtained from more spatially homogeneous district-level data. Country-level data also overestimate tf/2 ? 22 weeks, compared with 8-10 weeks from district-level data. Additionally, estimates for the duration of individual infectiousness is around two weeks from spatially inhomogeneous country-level data compared with 2.4-4.5 weeks from spatially more homogeneous district-level data, which estimates are rather high compared with most values reported in the literature. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.
Project description:BACKGROUND:The number of dengue fever (DF) cases and the number of dengue outbreaks have increased in recent years in Zhejiang Province, China. An unexpected dengue outbreak was reported in Hangzhou in 2017 and caused more than one thousand dengue cases. This study was undertaken to evaluate the effectiveness of the actual control measures, estimate the proportion of inapparent infections during this outbreak and simulate epidemic development based on different levels of control measures taking inapparent infections into consideration. METHODS:The epidemic data of dengue cases in Hangzhou, Zhejiang Province, in 2017 and the number of the people exposed to the outbreaks were obtained from the China Information Network System of Disease Prevention and Control. The epidemic without intervention measures was used to estimate the unknown parameters. A susceptible-exposed-infectious/inapparent-recovered (SEIAR) model was used to estimate the effectiveness of the control interventions. The inapparent infections were also evaluated at the same time. RESULTS:In total, 1137 indigenous dengue cases were reported in Hangzhou in 2017. The number of indigenous dengue cases was estimated by the SEIAR model. This number was predicted to reach 6090 by the end of November 2, 2017, if no control measures were implemented. The total number of reported cases was reduced by 81.33% in contrast to the estimated incidence without intervention. The number of average daily inapparent cases was 10.18 times higher than the number of symptomatic cases. The earlier and more rigorously the vector control interventions were implemented, the more effective they were. The results showed that implementing vector control continuously for more than twenty days was more effective than every few days of implementation. Case isolation is not sufficient enough for epidemic control and only reduced the incidence by 38.10% in contrast to the estimated incidence without intervention, even if case isolation began seven days after the onset of the first case. CONCLUSIONS:The practical control interventions in the outbreaks that occurred in Hangzhou City were highly effective. The proportion of inapparent infections was large, and it played an important role in transmitting the disease during this epidemic. Early, continuous and high efficacy vector control interventions are necessary to limit the development of a dengue epidemic. Timely diagnosis and case reporting are important in the intervention at an early stage of the epidemic.
Project description:<h4>Background</h4>A number of highly effective COVID-19 vaccines have been developed and approved for mass vaccination. We evaluated the impact of vaccination on COVID-19 outbreak and disease outcomes in Ontario, Canada.<h4>Methods</h4>We used an agent-based transmission model and parameterized it with COVID-19 characteristics, demographics of Ontario, and age-specific clinical outcomes. We implemented a two-dose vaccination program according to tested schedules in clinical trials for Pfizer-BioNTech and Moderna vaccines, prioritizing healthcare workers, individuals with comorbidities, and those aged 65 and older. Daily vaccination rate was parameterized based on vaccine administration data. Using estimates of vaccine efficacy, we projected the impact of vaccination on the overall attack rate, hospitalizations, and deaths. We further investigated the effect of increased daily contacts at different stages during vaccination campaigns on outbreak control.<h4>Results</h4>Maintaining non-pharmaceutical interventions (NPIs) with an average of 74% reduction in daily contacts, vaccination with Pfizer-BioNTech and Moderna vaccines was projected to reduce hospitalizations by 27.3% (95% CrI: 22.3% - 32.4%) and 27.0% (95% CrI: 21.9% - 32.6%), respectively, over a one-year time horizon. The largest benefits of vaccination were observed in preventing deaths with reductions of 31.5% (95% CrI: 22.5% - 39.7%) and 31.9% (95% CrI: 22.0% - 41.4%) for Pfizer-BioNTech and Moderna vaccines, respectively, compared to no vaccination. We found that an increase of only 10% in daily contacts at the end of lockdown, when vaccination coverage with only one dose was 6%, would trigger a surge in the outbreak. Early relaxation of population-wide measures could lead to a substantial increase in the number of infections, potentially reaching levels observed during the peak of the second wave in Ontario.<h4>Conclusions</h4>Vaccination can substantially mitigate ongoing COVID-19 outbreaks. Sustaining population-wide NPIs, to allow for a sufficient increase in population-level immunity through vaccination, is essential to prevent future outbreaks.
Project description:<h4>Background</h4>While whole genome sequencing (WGS) may be more expensive than traditional testing and polymerase chain reaction (PCR), simple cost comparisons ignore the potential for WGS to reduce the societal costs of non-typhoidal Salmonella enterica through public health action to prevent illness.<h4>Methods</h4>We determined how many cases the use of WGS data would need to prevent to be cost-equal to serotyping and MLVA, or culture independent testing based on PCR in Australia. We then examined the costs and cost-savings of current typing methods compared with WGS in outbreak scenarios.<h4>Results</h4>A median of 275 (90% CrI -55-775) or 1.9% (90% CrI -0.4%-5.4%) of notified serotyped Salmonella cases would need to be prevented for WGS to be cost-equal to current typing methods and 1,550 (90% CrI 820-2,725) or 9.6% of all notified Salmonella cases would need to be prevented to be cost-equal to PCR. WGS is likely to result in cost savings in prolonged outbreaks, where data can support earlier public health action.<h4>Conclusions</h4>Despite currently having a higher cost per isolate, routine WGS of Salmonella was no more expensive than existing typing methods or PCR where >2% of illness was averted.
Project description:<h4>Objectives</h4>The ongoing COVID-19 pandemic expanded its geographic distribution through the movement of humans and caused subsequent local outbreaks. Hence, it is essential to investigate how human mobility and travel ban affect the transmission and spatial spread while minimizing the impact on social activities and national economics.<h4>Methods</h4>We developed a mobility network model for spatial epidemics, explicitly taking into account time-varying inter-province and inner-province population flows, spatial heterogeneity in terms of disease transmission, as well as the impact of media reports. The model is applied to study the epidemic of the dynamic network of 30 provinces of mainland China. The model was calibrated using the publicly available incidence and movement data.<h4>Results</h4>We estimated that the second outbreak occurred approximately on February 24, 2020, and the cumulative number of cases as of March 15, 2020, increased by 290.1% (95% CI: (255.3%, 324.9%)) without a travel ban in mainland China (excluding Hubei and Tibet). We found that intra-province travel contributes more to the increase of cumulative number of cases than inter-province travel.<h4>Conclusion</h4>Our quantitative and qualitative research results suggest that the strict travel ban has successfully prevented a severe secondary outbreak in mainland China, which provides solutions for many countries and regions experiencing secondary outbreaks of COVID-19.
Project description:Mosquito-borne diseases have become a significant health issue in many regions around the world. For tropical countries, diseases such as Dengue, Zika, and Chikungunya, became epidemic in the last decades. Health surveillance reports during this period were crucial in providing scientific-based information to guide decision making and resources allocation to control outbreaks. In this work, we perform data analysis of the last Chikungunya epidemics in the city of Rio de Janeiro by applying a compartmental mathematical model. Sensitivity analyses were performed in order to describe the contribution of each parameter to the outbreak incidence. We estimate the "basic reproduction number" for those outbreaks and predict the potential epidemic outbreak of the Mayaro virus. We also simulated several scenarios with different public interventions to decrease the number of infected people. Such scenarios should provide insights about possible strategies to control future outbreaks.