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We analyze an ensemble of n-sub-epidemic modeling for forecasting the trajectory of epidemics and pandemics. These ensemble modeling approaches, and models that integrate sub-epidemics to capture complex temporal dynamics, have demonstrated powerful forecasting capability. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. We systematically assess their calibration and short-term forecasting performance in short-term forecasts for the COVID-19 pandemic in the USA from late April 2020 to late February 2022. We compare their performance with two commonly used statistical ARIMA models. The best fit sub-epidemic model and three ensemble models constructed using the top-ranking sub-epidemic models consistently outperformed the ARIMA models in terms of the weighted interval score (WIS) and the coverage of the 95% prediction interval across the 10-, 20-, and 30-day short-term forecasts. In the 30-day forecasts, the average WIS ranged from 377.6 to 421.3 for the sub-epidemic models, whereas it ranged from 439.29 to 767.05 for the ARIMA models. Across 98 short-term forecasts, the ensemble model incorporating the top four ranking sub-epidemic models (Ensemble(4)) outperformed the (log) ARIMA model 66.3% of the time, and the ARIMA model 69.4% of the time in 30-day ahead forecasts in terms of the WIS. Ensemble(4) consistently yielded the best performance in terms of the metrics that account for the uncertainty of the predictions. This framework could be readily applied to investigate the spread of epidemics and pandemics beyond COVID-19, as well as other dynamic growth processes found in nature and society that would benefit from short-term predictions. SummaryThe COVID-19 pandemic has highlighted the urgent need to develop reliable tools to forecast the trajectory of epidemics and pandemics in near real-time. We describe and apply an ensemble n-sub-epidemic modeling framework for forecasting the trajectory of epidemics and pandemics. We systematically assess its calibration and short-term forecasting performance in weekly 10-30 days ahead forecasts for the COVID-19 pandemic in the USA from late April 2020 to late February 2022 and compare its performance with two different statistical ARIMA models. This framework demonstrated reliable forecasting performance and substantially outcompeted the ARIMA models. The forecasting performance was consistently best for the ensemble sub-epidemic models incorporating a higher number of top-ranking sub-epidemic models. The ensemble model incorporating the top four ranking sub-epidemic models consistently yielded the best performance, particularly in terms of the coverage rate of the 95% prediction interval and the weighted interval score. This framework can be applied to forecast other growth processes found in nature and society including the spread of information through social media.
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The SARS-CoV-2 omicron variant was first detected in South Africa in November, 2021 and has rapidly spread to more than 90 countries. The emergence of Omicron variant demands for enhanced genomic surveillance to track the mutation profile and spread of virus. In the current study, we have sequenced 15 whole-genome sequences of SARS-CoV-2 Omicron variant from Islamabad region of Pakistan. Among the 15 isolates, 66% were from Islamabad whereas 33% of cases had international travel history of United Kingdom, Maldives, South Africa, and Oman. The detection of Omicron in local community and in travelers highlights the need for rigorous screening at national level and at entry points in order to contain the spread of variant.
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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. Author summaryAs the COVID-19 pandemic continues to spread across Colombia, studies highlighting the intensity of the pandemic become imperative for appropriate resource allocation and informing public health policies. In this study we utilize mathematical models to infer the transmission dynamics of SARS-CoV-2 at the regional and national level as well as short-term forecast the COVID-19 epidemic trajectory. Moreover, we examine the geographic heterogeneity of the COVID-19 case incidence in Colombia along with the analysis of mobility and social media trends in relation to the observed COVID-19 case incidence in the country. The estimates of reproduction numbers at the national and regional level show sustained disease transmission as of May 30, 2021. Moreover, the 30-day ahead short-term forecasts for the most recent time-period (June 1-June 30, 2021) generated from the mathematical models needs to be interpreted with caution as the Richards model point towards a sustained decline in case incidence contrary to the GLM and sub-epidemic wave model. Nevertheless, the spatial analysis in Colombia shows distinct variations in incidence rate patterns across different departments that can be grouped into four distinct clusters. Lastly, the social media and mobility trends explain the occurrence of case resurgences over the time.
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Mexico has experienced one of the highest COVID-19 death rates in the world. A delayed response towards implementation of social distancing interventions until late March 2020 and a phased reopening of the country in June 2020 has facilitated sustained disease transmission in the region. Here, 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 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 spatial-temporal transmission patterns. The early estimates of reproduction number for Mexico were estimated between R[~]1.1-from genomic and case incidence data. Moreover, the mean estimate of R has fluctuated [~]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. We found that the sequential mortality forecasts from the GLM and Richards model predict downward trends in the number of deaths for all thirteen forecasts periods for Mexico and Mexico City. The sub-epidemic and IHME models predict more realistic stable trajectory of COVID-19 mortality trends for the last three forecast periods (09/21-10/21 - 09/28-10/27) for Mexico and Mexico City. Our findings support the view 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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In South Korea, 13,745 cases of COVID-19 have been reported as of 19 July, 2020. We used EpiEstim R package to investigate the time-varying reproduction numbers of the COVID-19 in the four most affected regions in South Korea: Seoul, Gyeonggi Province, Gyeongbuk Province, and Daegu. At the regional level, Seoul and Gyeonggi Province have experienced two waves with the first major peak of COVID-19 in early March, followed by the second wave in the first two weeks of June, with reproduction numbers in early May greater than 3.0. Gyeongbuk Province and Daegu are yet to experience a second wave of the disease, where the mean reproduction number reached values as high as 3.5-4.4. Our findings indicate that the loosening of the restrictions imposed by the government in May 2020 facilitated a second wave in the greater Seoul area. Article Summary LineThe loosening of the social distancing measures imposed by the Korean government in May 2020 has resulted in the second wave of COVID-19 in the greater Seoul area in the first two weeks of June, yielding reproduction numbers exceeding 3.0
ABSTRACT
Mathematical models have been widely used to understand the dynamics of the ongoing coronavirus disease 2019 (COVID-19) pandemic as well as to predict future trends and assess intervention strategies. The asynchronicity of infection patterns during this pandemic illustrates the need for models that can capture dynamics beyond a single-peak trajectory to forecast the worldwide spread and for the spread within nations and within other sub-regions at various geographic scales. Here, we demonstrate a five-parameter sub-epidemic wave modeling framework that provides a simple characterization of unfolding trajectories of COVID-19 epidemics that are progressing across the world at different spatial scales. We calibrate the model to daily reported COVID-19 incidence data to generate six sequential weekly forecasts for five European countries and five hotspot states within the United States. The sub-epidemic approach captures the rise to an initial peak followed by a wide range of post-peak behavior, ranging from a typical decline to a steady incidence level to repeated small waves for sub-epidemic outbreaks. We show that the sub-epidemic model outperforms a three-parameter Richards model, in terms of calibration and forecasting performance, and yields excellent short- and intermediate-term forecasts that are not attainable with other single-peak transmission models of similar complexity. Overall, this approach predicts that a relaxation of social distancing measures would result in continuing sub-epidemics and ongoing endemic transmission. We illustrate how this view of the epidemic could help data scientists and policymakers better understand and predict the underlying transmission dynamics of COVID-19, as early detection of potential sub-epidemics can inform model-based decisions for tighter distancing controls.
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Peru implemented strict social distancing measures during the early phase of the epidemic and is now experiencing one of the largest CoVID-19 epidemics in Latin America. Estimates of disease severity are an essential indicator to inform policy decisions about the intensity and duration of interventions needed to mitigate the outbreak. Here we derive delay-adjusted case fatality rates (aCFR) of CoVID-19 in a middle-income country in South America. We used government-reported time series of CoVID-19 cases and deaths stratified by age group and gender. Our estimates as of May 25, 2020, of the aCFR for men and women are 10.8% (95%CrI: 10.5-11.1%) and 6.5% (95%CrI: 6.2-6.8%), respectively, and an overall aCFR of 9.1% (95%CrI: 8.9-9.3%). Our results show that senior individuals are the most severely affected by CoVID-19, particularly men, with aCFR of almost 60% for those aged 80-years. We found that men have a significantly higher cumulative morbidity ratio than women across most age groups (proportion test, p-value< 0.001), with the exception of those aged 0-9 years. The COVID-19 epidemic is imposing a large mortality burden in Peru. Senior individuals, especially those who are older than 70 years of age, are being disproportionately affected by the COVID-19 pandemic.
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Since the detection of first case of COVID-19 in Chile on March 3rd, 2020, a total of 301019 cases including 6434 deaths have been reported in Chile as of July 7th, 2020. In this manuscript we estimate the reproduction number during the early transmission phase in Chile and study the effectiveness of control interventions by conducting short-term forecasts based on the early transmission dynamics of COVID-19. We also estimate the reproduction number and conduct short term forecasts for the most recent developments in the epidemic trajectory of COVID-19 in Chile (May 9th-July 7th, 2020) to study the effectiveness of re-imposition of lockdowns in the country. The incidence curve in Chile displays early sub-exponential growth dynamics with the scaling of growth parameter, p, estimated at 0.8 (95% CI: 0.7, 0.8) and the reproduction number, estimated at 1.8 (95% CI: 1.6, 1.9). Our analysis emphasizes that the control measures at the start of the epidemic significantly slowed down the spread of the virus. However, easing of the COVID-19 restrictions and spread of virus to the low income neighborhoods in May led to a new wave of infections, followed by the re-imposition of lockdowns in Santiago and other municipalities. These measures have decelerated the virus spread with R estimated at ~0.87(95% CI: 0.84, 0.89) as of July 7th, 2020. Our current findings point that the sustained transmission of SARS-CoV-2 in Chile is being brought under control. The COVID-19 epidemic followed an early sub-exponential growth trend (p ~0.8) that transformed into a linear growth trend (p ~0.5) as of July 7th, 2020. While the broad scale social distancing interventions have slowed the virus spread, the number of new COVID-19 cases continue to accrue, underscoring the need for persistent social distancing and active case detection and isolation efforts to bring epidemic under control. Author summaryIn context of the ongoing COVID-19 pandemic, Chile is one of the hardest hit countries in Latin America, struggling to contain the spread of the virus. In this manuscript we employ renewal equation to estimate the reproduction number for the early ascending phase of the COVID-19 epidemic and the most recent time period to guide the magnitude and intensity of the interventions required to combat the COVID-19 epidemic. We also generate short terms forecasts based on the epidemic trajectory using phenomenological models and assess counterfactual scenarios to understand any additional resources required to contain the spread of virus. Our results indicate early sustained transmission of SARS-CoV-2. However, the initial control measures at the start of the epidemic significantly slowed down the spread of the virus whose effect is visible two weeks after the implementation of interventions. Easing of the COVID-19 restrictions in May led to a new wave of infections, followed by the re-imposition of lockdowns in Santiago and other municipalities. While the broad scale social distancing interventions have slowed the most recent spread of the virus spread, the number of new COVID-19 cases continue to accrue, underscoring the need for persistent social distancing efforts to bring epidemic under control.
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The COVID-19 pandemic that emerged in Wuhan China has generated substantial morbidity and mortality impact around the world during the last four months. The daily trend in reported cases has been rapidly rising in Latin America since March 2020 with the great majority of the cases reported in Brazil followed by Peru as of April 15th, 2020. Although Peru implemented a range of social distancing measures soon after the confirmation of its first case on March 6th, 2020, the daily number of new COVID-19 cases continues to accumulate in this country. We assessed the early COVID-19 transmission dynamics and the effect of social distancing interventions in Lima, Peru. We estimated the reproduction number, R, during the early transmission phase in Lima from the daily series of imported and autochthonous cases by the date of symptoms onset as of March 30th, 2020. We also assessed the effect of social distancing interventions in Lima by generating short-term forecasts grounded on the early transmission dynamics before interventions were put in place. Prior to the implementation of the social distancing measures in Lima, the local incidence curve by the date of symptoms onset displays near exponential growth dynamics with the mean scaling of growth parameter, p, estimated at 0.9 (95%CI: 0.9,1.0) and the reproduction number at 2.3 (95% CI: 2.0, 2.5). Our analysis indicates that school closures and other social distancing interventions have helped slow down the spread of the novel coronavirus, with the nearly exponential growth trend shifting to an approximately linear growth trend soon after the broad scale social distancing interventions were put in place by the government. While the interventions appear to have slowed the transmission rate in Lima, the number of new COVID-19 cases continue to accumulate, highlighting the need to strengthen social distancing and active case finding efforts to mitigate disease transmission in the region. Peru COVID-19 working group O_TBL View this table: org.highwire.dtl.DTLVardef@113464dorg.highwire.dtl.DTLVardef@6c8ba2org.highwire.dtl.DTLVardef@434c63org.highwire.dtl.DTLVardef@4c0821org.highwire.dtl.DTLVardef@1a9c01e_HPS_FORMAT_FIGEXP M_TBL C_TBL
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We estimated the reproduction number of 2020 Iranian COVID-19 epidemic using two different methods: R0 was estimated at 4.4 (95% CI, 3.9, 4.9) (generalized growth model) and 3.50 (1.28, 8.14) (epidemic doubling time) (February 19 - March 1) while the effective R was estimated at 1.55 (1.06, 2.57) (March 6-19).
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Since 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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BackgroundThe ongoing COVID-19 epidemic that spread widely in China since December 2019 is now generating local transmission in multiple countries including Singapore as of February 27, 2020. This highlights the need to monitor in real time the transmission potential of COVID-19. In Singapore, four major COVID-19 case clusters have emerged thus far. MethodsHere we estimate the effective reproduction number, Rt, of COVID-19 in Singapore from the publicly available daily case series of imported and autochthonous cases by date of symptoms onset, after adjusting the local cases for reporting delays. We also derive the reproduction number from the distribution of cluster sizes using a branching process analysis. ResultsThe effective reproduction number peaked with a mean value [~]1.1 around February 2nd, 2020 and declined thereafter. As of February 27th, 2020, our most recent estimate of Rt is at 0.5 (95% CI: 0.2,0.7) while an estimate of the overall R based on cluster size distribution is at 0.7 (95% CI: 0.5, 0.9). ConclusionThe trajectory of the reproduction number in Singapore underscore the significant effects of containment efforts in Singapore while at the same time suggest the need to sustain social distancing and active case finding efforts to stomp out all active chains of transmission.
