An investigation of spatial-temporal patterns and predictions of the COVID-19 pandemic in Colombia, 2020-2021

Colombia announced the first case of severe acute respiratory syndrome coronavirus 2 on March 6, 2020. Since then, the country has reported a total of 4,240,982 cases and 106,544 deaths as of June 30, 2021. This motivates an investigation of the SARS-CoV-2 transmission dynamics at the national and regional level using case incidence data. Mathematical models are employed to estimate the transmission potential and perform short-term forecasts of the COVID-19 epidemic trajectory in Colombia. Furthermore, geographic heterogeneity of COVID-19 in Colombia is examined along with the analysis of mobility and social media trends, showing that the increase in mobility in July 2020 and January 2021 were correlated with surges in case incidence. The estimation of national and regional reproduction numbers shows sustained disease transmission during the early phase of the pandemic, exhibiting sub-exponential growth dynamics. Moreover, most recent estimates of reproduction number are >1.0 at the national and regional levels as of May 30, 2021. Further, the 30-day ahead short-term forecasts obtained from Richards model present a sustained decline in case counts in contrast to the sub-epidemic and GLM model. Nevertheless, our spatial analysis in Colombia shows distinct variations in incidence rate patterns across different departments that can be grouped into four distinct clusters. Lastly, the correlation of social media trends and adherence to social distancing measures is observed by the fact that a spike in the number of tweets indicating the stay-at-home orders was observed in November 2020 when the case incidence had already plateaued.

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Estimation of the Reproduction Number of Influenza A(H1N1)pdm2009 in South Korea Using Heterogeneous Models

10.21203/rs.3.rs-20387/v3 ◽

2020 ◽

Author(s):

Yunjeong Lee ◽

Dong Han Lee ◽

Hee-Dae Kwon ◽

Changsoo Kim ◽

jeehyun lee

Keyword(s):

South Korea ◽

Influenza A ◽

Reproduction Number ◽

Time Estimation ◽

Estimation Of Parameters ◽

Transmission Potential ◽

Incidence Data ◽

Reproduction Numbers ◽

Number Of Patients ◽

Influenza A H1n1

Abstract Background: The reproduction number is one of the most crucial parameters in determining disease dynamics, providing a summary measure of the transmission potential. However, estimating this value is particularly challenging owing to the characteristics of epidemic data, including non-reproducibility and incompleteness.Methods: In this study, we propose mathematical models with different population structures; each of these models can produce data on the number of cases of the influenza A(H1N1)pdm09 epidemic in South Korea. These structured models incorporating the heterogeneity of age and region are used to estimate the reproduction numbers at various terminal times. Subsequently, the age- and region-specific reproduction numbers are also computed to analyze the differences illustrated in the incidence data. Results: The basic SIR fails to provide a reasonable estimation of the reproduction numbers. The estimated values demonstrate a large variation and remains outside of the feasible range for the influenza, regardless of the time period for data. Real-time estimation using age- and region-structured models demonstrated that the effective reproduction number rose sharply during mid-October when the number of patients increased dramatically. The reproduction number fell below unity at the end of October and stayed lower than unity indicating that the epidemic starts decreasing, which is consistent with the incidence data. Conclusions: Numerical results reveal that the introduction of heterogeneity into the population to represent the general characteristics of dynamics is essential for the robust estimation of parameters.

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Transmission potential of COVID-19 in South Korea

10.1101/2020.02.27.20028829 ◽

2020 ◽

Cited By ~ 23

Author(s):

Eunha Shim ◽

Amna Tariq ◽

Wongyeong Choi ◽

Yiseul Lee ◽

Gerardo Chowell

Keyword(s):

South Korea ◽

Reproduction Number ◽

Social Distancing ◽

Transmission Potential ◽

Incidence Data ◽

Novel Coronavirus

AbstractSince the first identified individual of 2019 novel coronavirus (COVID-19) infection on Jan 20, 2020 in South Korea, the number of confirmed cases rapidly increased. As of Feb 26, 2020, 1,261 cases of COVID-19 including 12 deaths were confirmed in South Korea. Using the incidence data of COVID-19, we estimate the reproduction number at 1.5 (95% CI: 1.4-1.6), which indicates sustained transmission and support the implementation of social distancing measures to rapidly control the outbreak.

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Estimation of the Time-dependent Reproduction Number of Influenza A(H1N1)pdm2009 in South Korea

10.21203/rs.3.rs-20387/v1 ◽

2020 ◽

Author(s):

Yunjeong Lee ◽

Dong Han Lee ◽

Hee-Dae Kwon ◽

Changsoo Kim ◽

Jeehyun Lee

Keyword(s):

South Korea ◽

Influenza A ◽

Reproduction Number ◽

Time Estimation ◽

Time Dependent ◽

Estimation Of Parameters ◽

Transmission Potential ◽

Reproduction Numbers ◽

Influenza A H1n1 ◽

Feasible Range

Abstract Background: The reproduction number is one of the most crucial parameters in determining disease dynamics, providing a summary measure of the transmission potential. However, estimating this value is particularly challenging owing to the characteristics of epidemic data, including non-reproducibility and incompleteness.Methods: In this study, we propose mathematical models with different population structures; each of these models can produce data on the number of cases of the influenza A(H1N1)pdm09 epidemic in South Korea. These structured models incorporating the heterogeneity of age and region are used to estimate the time-dependent effective reproduction numbers. Subsequently, the age- and region-specific reproduction numbers are also computed to analyze the differences illustrated in the incidence data.Results: The basic SIR fails to provide a reasonable estimation of the reproduction numbers. The estimated values demonstrate a large variation and remains outside of the feasible range for the influenza, regardless of the time period for data. Real-time estimation using age- and region-structured models demonstrated that the effective reproduction number rose sharply during mid-October when the ㅜumber of patients increased dramatically. The reproduction number fell below unity at the end of October and stayed lower than unity indicating that the epidemic starts decreasing, which is consistent with the incidence data.Conclusions: Numerical results reveal that the introduction of heterogeneity into the population to represent the general characteristics of dynamics is essential for the robust estimation of parameters.

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Transmission potential of the novel coronavirus (COVID-19) onboard the Diamond Princess Cruises Ship, 2020

10.1101/2020.02.24.20027649 ◽

2020 ◽

Cited By ~ 8

Author(s):

Kenji Mizumoto ◽

Gerardo Chowell

Keyword(s):

Mathematical Modeling ◽

Time Series ◽

Reproduction Number ◽

Transmission Dynamics ◽

The Novel ◽

Epidemic Threshold ◽

Transmission Potential ◽

Incidence Data ◽

The Mean ◽

Novel Coronavirus

AbstractAn 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.

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COVID-19 spread and Weather in U.S. states: a cross-correlative study on summer-autumn 2020

10.1101/2021.01.29.21250793 ◽

2021 ◽

Author(s):

Emmanuel de Margerie

Keyword(s):

Disease Transmission ◽

Reproduction Number ◽

Absolute Humidity ◽

Cold Weather ◽

Outdoor Temperature ◽

Short Term ◽

Behavioral Adaptations ◽

Cross Covariance ◽

Social Restriction ◽

Indoor Spaces

AbstractAn effect of weather on sars-cov-2 transmission is regularly proposed as a putative cause of unexplained fluctuations of covid-19 new cases, but clear data supporting this hypothesis remains to be presented. Here I measured longitudinal time-series correlations between outdoor temperature, humidity and covid-19 reproduction number (Rt) in the 50 U.S. states (+DC). In order to mitigate the confounding influence of varying social restriction measures, the analysis spans a 5-month period during summer and autumn 2020 when restrictions were comparatively lower and more stable. I used a cross-covariance approach to account for a variable delay between infection and case report. For a delay near 11 days, most U.S. states exhibited a negative correlation between outdoor temperature and Rt, as well as between absolute humidity and Rt (mean r = −0.35). In 21 states, the correlation was strong (r < −0.5). Individual state data are presented, and associations between cold and/or dry weather episodes and short-term new case surges are proposed. After identifying potential confounding factors, I discuss 3 possible causal mechanisms that could explain a correlation between outdoor weather and indoor disease transmission: behavioral adaptations to cold weather, respiratory tract temperature, and the importing of outdoor absolute humidity to indoor spaces.

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The long-term dynamics of tuberculosis and other diseases with long serial intervals: implications of and for changing reproduction numbers

Epidemiology and Infection ◽

10.1017/s0950268898001113 ◽

1998 ◽

Vol 121 (2) ◽

pp. 309-324 ◽

Cited By ~ 36

Author(s):

E. VYNNYCKY ◽

P. E. M. FINE

Keyword(s):

Infectious Disease ◽

Basic Reproduction Number ◽

Reproduction Number ◽

England And Wales ◽

Transmission Potential ◽

Reproduction Numbers ◽

Serial Interval ◽

Net Reproduction ◽

Epidemiological Parameters ◽

The Basic Reproduction Number

The net and basic reproduction numbers are among the most widely-applied concepts in infectious disease epidemiology. A net reproduction number (the average number of secondary infectious cases resulting from each case in a given population) of above 1 is conventionally associated with an increase in incidence; the basic reproduction number (defined analogously for a ‘totally susceptible’ population) provides a standard measure of the ‘transmission potential’ of an infection. Using a model of the epidemiology of tuberculosis in England and Wales since 1900, we demonstrate that these measures are difficult to apply if disease can follow reinfection, and that they lose their conventional interpretations if important epidemiological parameters, such as the rate of contact between individuals, change over the time interval between successive cases in a chain of transmission (the serial interval).The net reproduction number for tuberculosis in England and Wales appears to have been approximately 1 from 1900 until 1950, despite concurrent declines in morbidity and mortality rates, and it declined rapidly in the second half of this century. The basic reproduction number declined from about 3 in 1900, reached 2 by 1950, and first fell below 1 in about 1960. Reductions in effective contact between individuals over this period, measured in terms of the average number of individuals to whom each case could transmit the infection, meant that the conventional basic reproduction number measure (which does not consider subsequent changes in epidemiological parameters) for a given year failed to reflect the ‘actual transmission potential’ of the infection. This latter property is better described by a variant of the conventional measure which takes secular trends in contact into account. These results are relevant for the interpretation of trends in any infectious disease for which epidemiological parameters change over time periods comparable to the infectious period, incubation period or serial interval.

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Estimating the under-ascertainment number of COVID-19 cases in Kano, Nigeria in the fourth week of April 2020: a modelling analysis of the early outbreak

10.21203/rs.3.rs-34391/v2 ◽

2020 ◽

Author(s):

Salihu S Musa ◽

Shi Zhao ◽

Nafiu Hussaini ◽

Zian Zuang ◽

Yushan Wu ◽

...

Keyword(s):

Exponential Growth ◽

Reproduction Number ◽

Arima Model ◽

Likelihood Estimation ◽

Reporting Rate ◽

Initial Growth ◽

Short Term ◽

First Case ◽

Modelling Analysis ◽

The Basic Reproduction Number

Abstract Background: The coronavirus disease 2019 (known as COVID-19) pandemic caused by Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) appeared in Wuhan, China has rapidly spread to over 200 countries and territories. In Nigeria, the Kano State Ministry of Health has confirmed its first case of COVID-19 on April 11, 2020, and since then there might have been issues of under-ascertainment that occurred roughly from 22 to 27 April 2020. As of 4 October 2020, there were 1738 reported COVID-19 cases in Kano with 54 associated deaths. In this work, we estimate the number of under-ascertainment cases and the basic reproduction number, B, of COVID-19 in Kano, Nigeria. We also predict the number of COVID-19 cases in the short term.Methods: We employ the exponential growth and modelled the outbreak curve of COVID-19 cases, in Kano, Nigeria from 11 to 30 April 2020. We estimated the number of under-ascertainment cases using the maximum likelihood estimation. We adopted the SI estimated for Hong Kong as approximations of the unknown SI for COVID-19 in Kano to estimate the a. We use ARIMA model to provide a short term (15 days) prediction of the COVID-19 cases in Kano, Nigeria.Results: We revealed that the initial growth phase mimic an exponential growth pattern. We found that the under-ascertainment was likely to have resulted in 213 (95% CI: 106−346) unreported cases from 22 to 27 April 2020. The reporting rate after 27 April 2020 increase up to 10-fold compared to the scenario from 22 to 27 April 2020 on average. We estimated the c of COVID-19 in Kano as 2.74 (95% CI: 2.53−2.96). We forecasted that the total number of COVID-19 cases in Kano to be 1067 (95% CI: 883, 2137) by June 6, 2020.Conclusion: The under-ascertainment likely exists during the fourth week of April, 2020 and should be regarded in the future analysis/investigation.

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Transmission dynamics and forecasts of the COVID-19 pandemic in Mexico, March-December 2020

PLoS ONE ◽

10.1371/journal.pone.0254826 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254826

Author(s):

Amna Tariq ◽

Juan M. Banda ◽

Pavel Skums ◽

Sushma Dahal ◽

Carlos Castillo-Garsow ◽

...

Keyword(s):

Mexico City ◽

Disease Transmission ◽

Growth Models ◽

State Level ◽

The State ◽

Mortality Trends ◽

Social Distancing ◽

Incidence Data ◽

Reproduction Numbers ◽

Mortality Forecasts

Mexico has experienced one of the highest COVID-19 mortality rates in the world. A delayed implementation of social distancing interventions in late March 2020 and a phased reopening of the country in June 2020 has facilitated sustained disease transmission in the region. In this study we systematically generate and compare 30-day ahead forecasts using previously validated growth models based on mortality trends from the Institute for Health Metrics and Evaluation for Mexico and Mexico City in near real-time. Moreover, we estimate reproduction numbers for SARS-CoV-2 based on the methods that rely on genomic data as well as case incidence data. Subsequently, functional data analysis techniques are utilized to analyze the shapes of COVID-19 growth rate curves at the state level to characterize the spatiotemporal transmission patterns of SARS-CoV-2. The early estimates of the reproduction number for Mexico were estimated between Rt ~1.1–1.3 from the genomic and case incidence data. Moreover, the mean estimate of Rt has fluctuated around ~1.0 from late July till end of September 2020. The spatial analysis characterizes the state-level dynamics of COVID-19 into four groups with distinct epidemic trajectories based on epidemic growth rates. Our results show that the sequential mortality forecasts from the GLM and Richards model predict a downward trend in the number of deaths for all thirteen forecast periods for Mexico and Mexico City. However, the sub-epidemic and IHME models perform better predicting a more realistic stable trajectory of COVID-19 mortality trends for the last three forecast periods (09/21-10/21, 09/28-10/27, 09/28-10/27) for Mexico and Mexico City. Our findings indicate that phenomenological models are useful tools for short-term epidemic forecasting albeit forecasts need to be interpreted with caution given the dynamic implementation and lifting of social distancing measures.

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COVID-19: Short term prediction model using daily incidence data

PLoS ONE ◽

10.1371/journal.pone.0250110 ◽

2021 ◽

Vol 16 (4) ◽

pp. e0250110

Author(s):

Hongwei Zhao ◽

Naveed N. Merchant ◽

Alyssa McNulty ◽

Tiffany A. Radcliff ◽

Murray J. Cote ◽

...

Keyword(s):

Disease Transmission ◽

Reproduction Number ◽

Transmission Rate ◽

Health Planning ◽

Time Interval ◽

Short Time Interval ◽

State Planning ◽

Incidence Data ◽

Short Term Prediction ◽

Incident Cases

Background Prediction of the dynamics of new SARS-CoV-2 infections during the current COVID-19 pandemic is critical for public health planning of efficient health care allocation and monitoring the effects of policy interventions. We describe a new approach that forecasts the number of incident cases in the near future given past occurrences using only a small number of assumptions. Methods Our approach to forecasting future COVID-19 cases involves 1) modeling the observed incidence cases using a Poisson distribution for the daily incidence number, and a gamma distribution for the series interval; 2) estimating the effective reproduction number assuming its value stays constant during a short time interval; and 3) drawing future incidence cases from their posterior distributions, assuming that the current transmission rate will stay the same, or change by a certain degree. Results We apply our method to predicting the number of new COVID-19 cases in a single state in the U.S. and for a subset of counties within the state to demonstrate the utility of this method at varying scales of prediction. Our method produces reasonably accurate results when the effective reproduction number is distributed similarly in the future as in the past. Large deviations from the predicted results can imply that a change in policy or some other factors have occurred that have dramatically altered the disease transmission over time. Conclusion We presented a modelling approach that we believe can be easily adopted by others, and immediately useful for local or state planning.

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Deciphering early-warning signals of the elimination and resurgence potential of SARS-CoV-2 from limited data at multiple scales

10.1101/2020.11.23.20236968 ◽

2020 ◽

Author(s):

Kris V Parag ◽

Benjamin J Cowling ◽

Christl A Donnelly

Keyword(s):

Early Warning ◽

Disease Transmission ◽

Multiple Scales ◽

Region Of Interest ◽

Warning Signals ◽

Early Warning Signals ◽

Transmission Potential ◽

Incidence Data ◽

The Difference ◽

Low Incidence

AbstractInferring the transmission potential of an infectious disease during the low-incidence period following an epidemic wave is crucial for preparedness. In this period, necessarily scarce data hamper existing inference methods, blurring early-warning signals essential for discriminating between the likelihoods of resurgence versus elimination. Advanced insight into whether a region of interest will face elevating caseloads (requiring swift community-wide interventions) or achieve local elimination (allowing interventions to be relaxed or refocussed on controlling the importation of infections), can be the difference between decisive and ineffective policy. We propose a novel early-warning framework that formally maximises information extracted from low-incidence data to robustly infer the chances of sustained local transmission or elimination in real time, at any desired scale of investigation. Applying this framework, we decipher previously hidden disease-transmission signals from the prolonged low-incidence COVID-19 data of New Zealand, Hong Kong and Victoria state, Australia. We uncover how timely interventions averted dangerous, resurgent waves of COVID-19 and support official declarations of elimination. Across these locations, we obtain strong evidence for the effectiveness of rapid and adaptive COVID-19 responses.

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