Model-free estimation of COVID-19 transmission dynamics from a complete outbreak

New Zealand had 1499 cases of COVID-19 before eliminating transmission of the virus. Extensive contract tracing during the outbreak has resulted in a dataset of epidemiologically linked cases. This data contains useful information about the transmission dynamics of the virus, its dependence on factors such as age, and its response to different control measures. We use Monte-Carlo network construction techniques to provide an estimate of the number of secondary cases for every individual infected during the outbreak. We then apply standard statistical techniques to quantify differences between groups of individuals. Children under 10 years old are significantly under-represented in the case data. Children infected fewer people on average and had a lower probability of transmitting the disease in comparison to adults and the elderly. Imported cases infected fewer people on average and also had a lower probability of transmitting than domestically acquired cases. Superspreading is a significant contributor to the epidemic dynamics, with 20% of cases among adults responsible for 65–85% of transmission. Subclinical cases infected fewer individuals than clinical cases. After controlling for outliers serial intervals were approximated with a normal distribution (μ = 4.4 days, σ = 4.7 days). Border controls and strong social distancing measures, particularly when targeted at superspreading, play a significant role in reducing the spread of COVID-19.

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Model-free estimation of COVID-19 transmission dynamics from a complete outbreak

10.1101/2020.07.21.20159335 ◽

2020 ◽

Cited By ~ 1

Author(s):

Alex James ◽

Michael J Plank ◽

Shaun Hendy ◽

Rachelle N Binny ◽

Audrey Lustig ◽

...

Keyword(s):

New Zealand ◽

Attack Rate ◽

The Elderly ◽

Control Measures ◽

Transmission Dynamics ◽

Epidemic Dynamics ◽

Model Free ◽

Construction Techniques ◽

Imported Cases ◽

Border Controls

Background New Zealand had 1499 cases of COVID-19 before eliminating transmission of the virus. Extensive contract tracing during the outbreak has resulted in a dataset of epidemiologically linked cases. This data contains useful information about the transmission dynamics of the virus, its dependence on factors such as age, and its response to different control measures. Method We use Monte-Carlo network construction techniques to provide an estimate of the number of secondary cases for every individual infected during the outbreak. We then apply standard statistical techniques to quantify differences between groups of individuals. Findings Children under 10 years old are significantly under-represented in the case data. Children infected fewer people on average and had a lower secondary attack rate in comparison to adults and the elderly. Imported cases infected fewer people on average and had a lower secondary attack rate than domestically acquired cases. Superspreading is a significant contributor to the epidemic dynamics, with 20% of cases among adults responsible for 65-85% of transmission. Asymptomatic cases infected fewer individuals than clinical cases. Serial intervals are approximately normally distributed (μ=5.0 days, σ=5.7 days). Early isolation and quarantine of cases reduced secondary transmission rates. Interpretation Border controls and strong social distancing measures, particularly when targeted at superspreading, play a significant role in reducing the spread of COVID-19. Funding Te Pūnaha Matatini, the New Zealand Centre of Research Excellence in complex systems. New Zealand Ministry of Business, Innovation and Employment.

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Transmission dynamics, serial interval and epidemiology of COVID-19 diseases in Hong Kong under different control measures

Wellcome Open Research ◽

10.12688/wellcomeopenres.15896.2 ◽

2020 ◽

Vol 5 ◽

pp. 91

Author(s):

Yung-Wai Desmond Chan ◽

Stefan Flasche ◽

Tin-Long Terence Lam ◽

Mei-Hung Joanna Leung ◽

Miu-Ling Wong ◽

...

Keyword(s):

Hong Kong ◽

Population Level ◽

Credible Interval ◽

Control Measures ◽

Transmission Dynamics ◽

Local Data ◽

First Case ◽

Serial Interval ◽

Imported Cases ◽

Epidemiological Characteristics

Background: The outbreak of coronavirus disease 2019 (COVID-19) started in Wuhan, China in late December 2019, and subsequently became a pandemic. Hong Kong had implemented a series of control measures since January 2020, including enhanced surveillance, isolation and quarantine, border control and social distancing. Hong Kong recorded its first case on 23 January 2020, who was a visitor from Wuhan. We analysed the surveillance data of COVID-19 to understand the transmission dynamics and epidemiology in Hong Kong. Methods: We constructed the epidemic curve of daily COVID-19 incidence from 23 January to 6 April 2020 and estimated the time-varying reproduction number (Rt) with the R package EpiEstim, with serial interval computed from local data. We described the demographic and epidemiological characteristics of reported cases. We computed weekly incidence by age and residential district to understand the spatial and temporal transmission of the disease. Results: COVID-19 disease in Hong Kong was characterised with local cases and clusters detected after two waves of importations, first in late January (week 4 to 6) and the second one in early March (week 9 to 10). The Rt increased to approximately 2 95% credible interval (CI): 0.3-3.3) and approximately 1 (95%CI: 0.2-1.7), respectively, following these importations; it decreased to below 1 afterwards from weeks 11 to 13, which coincided with the implementation, modification and intensification of different control measures. Compared to local cases, imported cases were younger (mean age: 52 years among local cases vs 35 years among imported cases), had a lower proportion of underlying disease (9% vs 5%) and severe outcome (13% vs 5%). Cases were recorded in all districts but the incidence was highest in those in the Hong Kong Island region. Conclusions: Stringent and sustained public health measures at population level could contain the COVID-19 disease at a relatively low level.

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Characterizing superspreading events and age-specific infectiousness of SARS-CoV-2 transmission in Georgia, USA

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2011802117 ◽

2020 ◽

Vol 117 (36) ◽

pp. 22430-22435 ◽

Cited By ~ 13

Author(s):

Max S. Y. Lau ◽

Bryan Grenfell ◽

Michael Thomas ◽

Michael Bryan ◽

Kristin Nelson ◽

...

Keyword(s):

Rural Areas ◽

The Elderly ◽

Control Measures ◽

Transmission Dynamics ◽

Reproductive Number ◽

Individual Level ◽

Mobility Data ◽

Multiple Data ◽

Main Driver ◽

Associated Risk Factors

It is imperative to advance our understanding of heterogeneities in the transmission of SARS-CoV-2 such as age-specific infectiousness and superspreading. To this end, it is important to exploit multiple data streams that are becoming abundantly available during the pandemic. In this paper, we formulate an individual-level spatiotemporal mechanistic framework to integrate individual surveillance data with geolocation data and aggregate mobility data, enabling a more granular understanding of the transmission dynamics of SARS-CoV-2. We analyze reported cases, between March and early May 2020, in five (urban and rural) counties in the state of Georgia. First, our results show that the reproductive number reduced to below one in about 2 wk after the shelter-in-place order. Superspreading appears to be widespread across space and time, and it may have a particularly important role in driving the outbreak in rural areas and an increasing importance toward later stages of outbreaks in both urban and rural settings. Overall, about 2% of cases were directly responsible for 20% of all infections. We estimate that the infected nonelderly cases (<60 y) may be 2.78 [2.10, 4.22] times more infectious than the elderly, and the former tend to be the main driver of superspreading. Our results improve our understanding of the natural history and transmission dynamics of SARS-CoV-2. More importantly, we reveal the roles of age-specific infectiousness and characterize systematic variations and associated risk factors of superspreading. These have important implications for the planning of relaxing social distancing and, more generally, designing optimal control measures.

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On the Transmission Dynamics of SARS-CoV-2 in a Temperate Climate

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18041660 ◽

2021 ◽

Vol 18 (4) ◽

pp. 1660

Author(s):

Ioannis Kioutsioukis ◽

Nikolaos I. Stilianakis

Keyword(s):

Dynamic Change ◽

Control Measures ◽

Transmission Dynamics ◽

Temperate Climate ◽

Model Parameters ◽

Forecast Horizon ◽

Public Health Decision ◽

Epidemic Dynamics ◽

Positive Effects ◽

Household Crowding

An epidemiological model, which describes the transmission dynamics of SARS-CoV-2 under specific consideration of the incubation period including the population with subclinical infections and being infective is presented. The COVID-19 epidemic in Greece was explored through a Monte Carlo uncertainty analysis framework, and the optimal values for the parameters that determined the transmission dynamics could be obtained before, during, and after the interventions to control the epidemic. The dynamic change of the fraction of asymptomatic individuals was shown. The analysis of the modelling results at the intra-annual climatic scale allowed for in depth investigation of the transmission dynamics of SARS-CoV-2 and the significance and relative importance of the model parameters. Moreover, the analysis at this scale incorporated the exploration of the forecast horizon and its variability. Three discrete peaks were found in the transmission rates throughout the investigated period (15 February–15 December 2020). Two of them corresponded to the timing of the spring and autumn epidemic waves while the third one occurred in mid-summer, implying that relaxation of social distancing and increased mobility may have a strong effect on rekindling the epidemic dynamics offsetting positive effects from factors such as decreased household crowding and increased environmental ultraviolet radiation. In addition, the epidemiological state was found to constitute a significant indicator of the forecast reliability horizon, spanning from as low as few days to more than four weeks. Embedding the model in an ensemble framework may extend the predictability horizon. Therefore, it may contribute to the accuracy of health risk assessment and inform public health decision making of more efficient control measures.

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Transmission dynamics, serial interval and epidemiology of COVID-19 diseases in Hong Kong under different control measures

Wellcome Open Research ◽

10.12688/wellcomeopenres.15896.1 ◽

2020 ◽

Vol 5 ◽

pp. 91 ◽

Cited By ~ 4

Author(s):

Yung-Wai Desmond Chan ◽

Stefan Flasche ◽

Tin-Long Terence Lam ◽

Mei-Hung Joanna Leung ◽

Miu-Ling Wong ◽

...

Keyword(s):

Hong Kong ◽

Population Level ◽

Underlying Disease ◽

Control Measures ◽

Transmission Dynamics ◽

Local Data ◽

First Case ◽

Serial Interval ◽

Imported Cases ◽

Epidemiological Characteristics

Background: The outbreak of coronavirus disease 2019 (COVID-19) started in Wuhan, China in late December 2019, and subsequently became a pandemic. Hong Kong had implemented a series of control measures since January 2020, including enhanced surveillance, isolation and quarantine, border control and social distancing. Hong Kong recorded its first case on 23 January 2020, who was a visitor from Wuahn. We analysed the surveillance data of COVID-19 to understand the transmission dynamics and epidemiology in Hong Kong. Methods: Based on cases recorded from 23 January to 6 April 2020, we constructed the epidemic curve of daily COVID-19 incidence and used this data to estimate the time-varying reproduction number (Rt) with the R package EpiEstim, with serial interval computed from local data. We described the demographic and epidemiological characteristics of reported cases. We computed weekly incidence by age and residential district to understand the spatial and temporal transmission of the disease. Results: COVID-19 disease in Hong Kong was characterised with local cases and clusters detected after two waves of importations, first in late January and the second one in early March. The Rt increased to approximately 2 and approximately 1, respectively, following these importations; it decreased to below 1 afterwards, which coincided with the implementation, modification and intensification of different control measures. Compared to local cases, imported cases were younger (mean age: 52 years among local cases vs 35 years among imported cases), had a lower proportion of underlying disease (9% vs 5%) and severe outcome (13% vs 5%). Cases were recorded in all districts but the incidence was highest in those in the Hong Kong Island region. Conclusions: Stringent and sustained public health measures at population level could contain the COVID-19 disease at a relatively low level.

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Analysis of Intervention Effectiveness Using Early Outbreak Transmission Dynamics to Guide Future Pandemic Management and Decision-Making in Kuwait

10.1101/2021.01.07.21249409 ◽

2021 ◽

Author(s):

Michael G. Tyshenko ◽

Tamer Oraby ◽

Joseph Craig Longenecker ◽

Harri Vainio ◽

Janvier Gasana ◽

...

Keyword(s):

Decision Making ◽

Reproduction Number ◽

Control Measures ◽

World Health ◽

Transmission Model ◽

Mobility Data ◽

Imported Cases ◽

Community Transmission ◽

Health Organization ◽

Case Data

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a World Health Organization designated pandemic that can result in severe symptoms and death that disproportionately affects older patients or those with comorbidities. Kuwait reported its first imported cases of COVID-19 on February 24, 2020. Analysis of data from the first three months of community transmission of the COVID-19 outbreak in Kuwait can provide important guidance for decision-making when dealing with future SARS-CoV-2 epidemic wave management. The analysis of intervention scenarios can help to evaluate the possible impacts of various outbreak control measures going forward which aim to reduce the effective reproduction number during the initial outbreak wave. Herein we use a modified susceptible-exposed-asymptomatic-infectious-removed (SEAIR) transmission model to estimate the outbreak dynamics of SARS-CoV-2 transmission in Kuwait. We fit case data from the first 96 days in the model to estimate the basic reproduction number and used Google mobility data to refine community contact matrices. The SEAIR modelled scenarios allow for the analysis of various interventions to determine their effectiveness. The model can help inform future pandemic wave management, not only in Kuwait but for other countries as well.

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Accounting for imported cases in estimating the time-varying reproductive number of COVID-19

10.1101/2021.02.09.21251416 ◽

2021 ◽

Author(s):

Tim K. Tsang ◽

Peng Wu ◽

Eric H. Y. Lau ◽

Benjamin J. Cowling

Keyword(s):

Public Health ◽

Control Measures ◽

Transmission Dynamics ◽

Reproductive Number ◽

Considerable Proportion ◽

Time Varying ◽

Health Measures ◽

Imported Cases ◽

The Impact ◽

Local Transmission

ABSTRACTBackgroundEstimating the time-varying reproductive number, Rt, is critical for monitoring transmissibility of an emerging infectious disease during outbreaks. When local transmission is effectively suppressed, imported cases could substantially impact transmission dynamics.MethodsWe developed methodology to estimate separately the Rt for local cases and imported cases, since certain public health measures aim only to reduce onwards transmission from imported cases. We applied the framework to data on COVID-19 outbreaks in Hong Kong.ResultsWe estimated that the Rt for local cases decreased from above one in the early phase of outbreak to below one after tightening of public health measures. Assuming the same infectiousness of local and imported cases underestimated Rt for local cases due to control measures targeting travelers.ConclusionsWhen a considerable proportion of all cases are imported, the impact of imported cases in estimating Rt is critical. The methodology described here can allow for differential infectiousness of local imported cases.

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Parameter-dependent transmission dynamics and optimal control of foot and mouth disease in a contaminated environment

Journal of the Egyptian Mathematical Society ◽

10.1186/s42787-019-0058-1 ◽

2019 ◽

Vol 27 (1) ◽

Cited By ~ 1

Author(s):

Francis Mugabi ◽

Joseph Mugisha ◽

Betty Nannyonga ◽

Henry Kasumba ◽

Margaret Tusiime

Keyword(s):

Optimal Control ◽

Deterministic Model ◽

Foot And Mouth Disease ◽

Decay Rates ◽

Mouth Disease ◽

Control Measures ◽

Transmission Dynamics ◽

Optimality System ◽

Foot And Mouth ◽

Environmental Decontamination

AbstractThe problem of foot and mouth disease (FMD) is of serious concern to the livestock sector in most nations, especially in developing countries. This paper presents the formulation and analysis of a deterministic model for the transmission dynamics of FMD through a contaminated environment. It is shown that the key parameters that drive the transmission of FMD in a contaminated environment are the shedding, transmission, and decay rates of the virus. Using numerical results, it is depicted that the host-to-host route is more severe than the environmental-to-host route. The model is then transformed into an optimal control problem. Using the Pontryagin’s Maximum Principle, the optimality system is determined. Utilizing a gradient type algorithm with projection, the optimality system is solved for three control strategies: optimal use of vaccination, environmental decontamination, and a combination of vaccination and environmental decontamination. Results show that a combination of vaccination and environmental decontamination is the most optimal strategy. These results indicate that if vaccination and environmental decontamination are used optimally during an outbreak, then FMD transmission can be controlled. Future studies focusing on the control measures for the transmission of FMD in a contaminated environment should aim at reducing the transmission and the shedding rates, while increasing the decay rate.

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European Web-Based Platform for Recording International Health Regulations Ship Sanitation Certificates: Results and Perspectives

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph15091833 ◽

2018 ◽

Vol 15 (9) ◽

pp. 1833

Author(s):

Varvara Mouchtouri ◽

Diederik Van Reusel ◽

Nikolaos Bitsolas ◽

Antonis Katsioulis ◽

Raf Van den Bogaert ◽

...

Keyword(s):

Information System ◽

Data Exchange ◽

International Health ◽

Water Quality Monitoring ◽

Control Measures ◽

World Health ◽

Lower Probability ◽

International Health Regulations ◽

Eu Member States ◽

Health Regulations

The purpose of this study was to report the data analysis results from the International Health Regulations (2005) Ship Sanitation Certificates (SSCs), recorded in the European Information System (EIS). International sea trade and population movements by ships can contribute to the global spread of diseases. SSCs are issued to ensure the implementation of control measures if a public health risk exists on board. EIS designed according to the World Health Organization (WHO) “Handbook for Inspection of Ships and Issuance of SSC”. Inspection data were recorded and SSCs issued by inspectors working at European ports were analysed. From July 2011–February 2017, 107 inspectors working at 54 ports in 11 countries inspected 5579 ships. Of these, there were 29 types under 85 flags (including 19 EU Member States flags). As per IHR (2005) 10,281 Ship Sanitation Control Exception Certificates (SSCECs) and 296 Ship Sanitation Control Certificates (SSCCs) were issued, 74 extensions to existing SSCs were given, 7565 inspection findings were recorded, and 47 inspections were recorded without issuing an SSC. The most frequent inspection findings were the lack of potable water quality monitoring reports (23%). Ships aged ≥12 years (odds ratio, OR = 1.77, 95% confidence intervals, CI = 1.37–2.29) with an absence of cargo at time of inspection (OR = 3.36, 95% CI = 2.51–4.50) had a higher probability of receiving an SSCC, while ships under the EU MS flag had a lower probability of having inspection findings (OR = 0.72, 95% CI = 0.66–0.79). Risk factors to prioritise the inspections according to IHR were identified by using the EIS. A global information system, or connection of national or regional information systems and data exchange, could help to better implement SSCs using common standards and procedures.

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Evolving epidemiology of novel coronavirus diseases 2019 and possible interruption of local transmission outside Hubei Province in China: a descriptive and modeling study

10.1101/2020.02.21.20026328 ◽

2020 ◽

Cited By ~ 19

Author(s):

Juanjuan Zhang ◽

Maria Litvinova ◽

Wei Wang ◽

Yan Wang ◽

Xiaowei Deng ◽

...

Keyword(s):

Age Groups ◽

Mainland China ◽

The Elderly ◽

Hubei Province ◽

Epidemic Threshold ◽

Epidemic Dynamics ◽

Serial Interval ◽

The Mean ◽

Novel Coronavirus ◽

Local Transmission

AbstractBackgroundThe COVID-19 epidemic originated in Wuhan City of Hubei Province in December 2019 and has spread throughout China. Understanding the fast evolving epidemiology and transmission dynamics of the outbreak beyond Hubei would provide timely information to guide intervention policy.MethodsWe collected individual information on 8,579 laboratory-confirmed cases from official publically sources reported outside Hubei in mainland China, as of February 17, 2020. We estimated the temporal variation of the demographic characteristics of cases and key time-to-event intervals. We used a Bayesian approach to estimate the dynamics of the net reproduction number (Rt) at the provincial level.ResultsThe median age of the cases was 44 years, with an increasing of cases in younger age groups and the elderly as the epidemic progressed. The delay from symptom onset to hospital admission decreased from 4.4 days (95%CI: 0.0-14.0) until January 27 to 2.6 days (0.0-9.0) from January 28 to February 17. The mean incubation period was estimated at 5.2 days (1.8-12.4) and the mean serial interval at 5.1 days (1.3-11.6). The epidemic dynamics in provinces outside Hubei was highly variable, but consistently included a mix of case importations and local transmission. We estimate that the epidemic was self-sustained for less than three weeks with Rt reaching peaks between 1.40 (1.04-1.85) in Shenzhen City of Guangdong Province and 2.17 (1.69-2.76) in Shandong Province. In all the analyzed locations (n=10) Rt was estimated to be below the epidemic threshold since the end of January.ConclusionOur findings suggest that the strict containment measures and movement restrictions in place may contribute to the interruption of local COVID-19 transmission outside Hubei Province. The shorter serial interval estimated here implies that transmissibility is not as high as initial estimates suggested.

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