151
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Brizzi A, O'Driscoll M, Dorigatti I. Refining reproduction number estimates to account for unobserved generations of infection in emerging epidemics. Clin Infect Dis 2022; 75:e114-e121. [PMID: 35176766 PMCID: PMC9402635 DOI: 10.1093/cid/ciac138] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Indexed: 12/14/2022] Open
Abstract
Background Estimating the transmissibility of infectious diseases is key to inform situational awareness and for response planning. Several methods tend to overestimate the basic (R0) and effective (Rt) reproduction numbers during the initial phases of an epidemic. In this work we explore the impact of incomplete observations and underreporting of the first generations of infections during the initial epidemic phase. Methods We propose a debiasing procedure that utilizes a linear exponential growth model to infer unobserved initial generations of infections and apply it to EpiEstim. We assess the performance of our adjustment using simulated data, considering different levels of transmissibility and reporting rates. We also apply the proposed correction to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence data reported in Italy, Sweden, the United Kingdom, and the United States. Results In all simulation scenarios, our adjustment outperforms the original EpiEstim method. The proposed correction reduces the systematic bias, and the quantification of uncertainty is more precise, as better coverage of the true R0 values is achieved with tighter credible intervals. When applied to real-world data, the proposed adjustment produces basic reproduction number estimates that closely match the estimates obtained in other studies while making use of a minimal amount of data. Conclusions The proposed adjustment refines the reproduction number estimates obtained with the current EpiEstim implementation by producing improved, more precise estimates earlier than with the original method. This has relevant public health implications.
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Affiliation(s)
- Andrea Brizzi
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Megan O'Driscoll
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom.,Department of Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis and Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
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152
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O'Dea EB, Drake JM. A semi-parametric, state-space compartmental model with time-dependent parameters for forecasting COVID-19 cases, hospitalizations and deaths. J R Soc Interface 2022; 19:20210702. [PMID: 35167769 PMCID: PMC8847000 DOI: 10.1098/rsif.2021.0702] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Short-term forecasts of the dynamics of coronavirus disease 2019 (COVID-19) in the period up to its decline following mass vaccination was a task that received much attention but proved difficult to do with high accuracy. However, the availability of standardized forecasts and versioned datasets from this period allows for continued work in this area. Here, we introduce the Gaussian infection state space with time dependence (GISST) forecasting model. We evaluate its performance in one to four weeks ahead forecasts of COVID-19 cases, hospital admissions and deaths in the state of California made with official reports of COVID-19, Google’s mobility reports and vaccination data available each week. Evaluation of these forecasts with a weighted interval score shows them to consistently outperform a naive baseline forecast and often score closer to or better than a high-performing ensemble forecaster. The GISST model also provides parameter estimates for a compartmental model of COVID-19 dynamics, includes a regression submodel for the transmission rate and allows for parameters to vary over time according to a random walk. GISST provides a novel, balanced combination of computational efficiency, model interpretability and applicability to large multivariate datasets that may prove useful in improving the accuracy of infectious disease forecasts.
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Affiliation(s)
- Eamon B O'Dea
- Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
| | - John M Drake
- Odum School of Ecology and Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
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153
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Ould Setti M, Tollis S. In-Depth Correlation Analysis of SARS-CoV-2 Effective Reproduction Number and Mobility Patterns: Three Groups of Countries. J Prev Med Public Health 2022; 55:134-143. [PMID: 35391525 PMCID: PMC8995941 DOI: 10.3961/jpmph.21.522] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/13/2021] [Indexed: 11/09/2022] Open
Abstract
Objectives Many governments have imposed—and are still imposing—mobility restrictions to contain the coronavirus disease 2019 (COVID-19) pandemic. However, there is no consensus on whether policy-induced reductions of human mobility effectively reduce the effective reproduction number (Rt) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several studies based on country-restricted data reported conflicting trends in the change of the SARS-CoV-2 Rt following mobility restrictions. The objective of this study was to examine, at the global scale, the existence of regional specificities in the correlations between Rt and human mobility. Methods We computed the Rt of SARS-CoV-2 using data on worldwide infection cases reported by the Johns Hopkins University, and analyzed the correlation between Rt and mobility indicators from the Google COVID-19 Community Mobility Reports in 125 countries, as well as states/regions within the United States, using the Pearson correlation test, linear modeling, and quadratic modeling. Results The correlation analysis identified countries where Rt negatively correlated with residential mobility, as expected by policymakers, but also countries where Rt positively correlated with residential mobility and countries with more complex correlation patterns. The correlations between Rt and residential mobility were non-linear in many countries, indicating an optimal level above which increasing residential mobility is counterproductive. Conclusions Our results indicate that, in order to effectively reduce viral circulation, mobility restriction measures must be tailored by region, considering local cultural determinants and social behaviors. We believe that our results have the potential to guide differential refinement of mobility restriction policies at a country/regional resolution.
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Affiliation(s)
- Mounir Ould Setti
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio,
Finland
- Global Database Studies, IQVIA, Espoo,
Finland
| | - Sylvain Tollis
- Institute of Biomedicine, University of Eastern Finland, Kuopio,
Finland
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154
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Hart WS, Abbott S, Endo A, Hellewell J, Miller E, Andrews N, Maini PK, Funk S, Thompson RN. Inference of the SARS-CoV-2 generation time using UK household data. eLife 2022; 11:70767. [PMID: 35138250 PMCID: PMC8967386 DOI: 10.7554/elife.70767] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 02/07/2022] [Indexed: 11/17/2022] Open
Abstract
The distribution of the generation time (the interval between individuals becoming infected and transmitting the virus) characterises changes in the transmission risk during SARS-CoV-2 infections. Inferring the generation time distribution is essential to plan and assess public health measures. We previously developed a mechanistic approach for estimating the generation time, which provided an improved fit to data from the early months of the COVID-19 pandemic (December 2019-March 2020) compared to existing models (Hart et al., 2021). However, few estimates of the generation time exist based on data from later in the pandemic. Here, using data from a household study conducted from March to November 2020 in the UK, we provide updated estimates of the generation time. We considered both a commonly used approach in which the transmission risk is assumed to be independent of when symptoms develop, and our mechanistic model in which transmission and symptoms are linked explicitly. Assuming independent transmission and symptoms, we estimated a mean generation time (4.2 days, 95% credible interval 3.3–5.3 days) similar to previous estimates from other countries, but with a higher standard deviation (4.9 days, 3.0–8.3 days). Using our mechanistic approach, we estimated a longer mean generation time (5.9 days, 5.2–7.0 days) and a similar standard deviation (4.8 days, 4.0–6.3 days). As well as estimating the generation time using data from the entire study period, we also considered whether the generation time varied temporally. Both models suggest a shorter mean generation time in September-November 2020 compared to earlier months. Since the SARS-CoV-2 generation time appears to be changing, further data collection and analysis is necessary to continue to monitor ongoing transmission and inform future public health policy decisions.
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Affiliation(s)
| | - Sam Abbott
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Akira Endo
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Joel Hellewell
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Elizabeth Miller
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Nick Andrews
- Data and Analytical Sciences, UK Health Security Agency, Public Health England, London, United Kingdom
| | - Philip K Maini
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Sebastian Funk
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Robin N Thompson
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
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155
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Dablander F, Heesterbeek H, Borsboom D, Drake JM. Overlapping timescales obscure early warning signals of the second COVID-19 wave. Proc Biol Sci 2022; 289:20211809. [PMID: 35135355 PMCID: PMC8825995 DOI: 10.1098/rspb.2021.1809] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 01/13/2022] [Indexed: 11/12/2022] Open
Abstract
Early warning indicators based on critical slowing down have been suggested as a model-independent and low-cost tool to anticipate the (re)emergence of infectious diseases. We studied whether such indicators could reliably have anticipated the second COVID-19 wave in European countries. Contrary to theoretical predictions, we found that characteristic early warning indicators generally decreased rather than increased prior to the second wave. A model explains this unexpected finding as a result of transient dynamics and the multiple timescales of relaxation during a non-stationary epidemic. Particularly, if an epidemic that seems initially contained after a first wave does not fully settle to its new quasi-equilibrium prior to changing circumstances or conditions that force a second wave, then indicators will show a decreasing rather than an increasing trend as a result of the persistent transient trajectory of the first wave. Our simulations show that this lack of timescale separation was to be expected during the second European epidemic wave of COVID-19. Overall, our results emphasize that the theory of critical slowing down applies only when the external forcing of the system across a critical point is slow relative to the internal system dynamics.
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Affiliation(s)
- Fabian Dablander
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Hans Heesterbeek
- Department of Population Health Sciences, Utrecht University, Utrecht, The Netherlands
| | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, GA, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
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156
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Genomic epidemiology of SARS-CoV-2 under an elimination strategy in Hong Kong. Nat Commun 2022; 13:736. [PMID: 35136039 PMCID: PMC8825829 DOI: 10.1038/s41467-022-28420-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 01/19/2022] [Indexed: 12/15/2022] Open
Abstract
Hong Kong employed a strategy of intermittent public health and social measures alongside increasingly stringent travel regulations to eliminate domestic SARS-CoV-2 transmission. By analyzing 1899 genome sequences (>18% of confirmed cases) from 23-January-2020 to 26-January-2021, we reveal the effects of fluctuating control measures on the evolution and epidemiology of SARS-CoV-2 lineages in Hong Kong. Despite numerous importations, only three introductions were responsible for 90% of locally-acquired cases. Community outbreaks were caused by novel introductions rather than a resurgence of circulating strains. Thus, local outbreak prevention requires strong border control and community surveillance, especially during periods of less stringent social restriction. Non-adherence to prolonged preventative measures may explain sustained local transmission observed during wave four in late 2020 and early 2021. We also found that, due to a tight transmission bottleneck, transmission of low-frequency single nucleotide variants between hosts is rare.
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157
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Wilasang C, Jitsuk NC, Sararat C, Modchang C. Reconstruction of the transmission dynamics of the first COVID-19 epidemic wave in Thailand. Sci Rep 2022; 12:2002. [PMID: 35132106 PMCID: PMC8821624 DOI: 10.1038/s41598-022-06008-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 01/19/2022] [Indexed: 12/14/2022] Open
Abstract
Thailand was the first country reporting the first Coronavirus disease 2019 (COVID-19) infected individual outside mainland China. Here we delineated the course of the COVID-19 outbreak together with the timeline of the control measures and public health policies employed by the Thai government during the first wave of the COVID-19 outbreak in Thailand. Based on the comprehensive epidemiological data, we reconstructed the dynamics of COVID-19 transmission in Thailand using a stochastic modeling approach. Our stochastic model incorporated the effects of individual heterogeneity in infectiousness on disease transmission, which allows us to capture relevant features of superspreading events. We found that our model could accurately capture the transmission dynamics of the first COVID-19 epidemic wave in Thailand. The model predicted that at the end of the first wave, the number of cumulative confirmed cases was 3091 (95%CI: 2782-3400). We also estimated the time-varying reproduction number (Rt) during the first epidemic wave. We found that after implementing the nationwide interventions, the Rt in Thailand decreased from the peak value of 5.67 to a value below one in less than one month, indicating that the control measures employed by the Thai government during the first COVID-19 epidemic wave were effective. Finally, the effects of transmission heterogeneity and control measures on the likelihood of outbreak extinction were also investigated.
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Affiliation(s)
- Chaiwat Wilasang
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
| | - Natcha C Jitsuk
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
| | - Chayanin Sararat
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
| | - Charin Modchang
- Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand. .,Centre of Excellence in Mathematics, CHE, Bangkok, 10400, Thailand. .,Thailand Center of Excellence in Physics, CHE, 328 Si Ayutthaya Road, Bangkok, 10400, Thailand.
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158
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Bsat R, Chemaitelly H, Coyle P, Tang P, Hasan MR, Al Kanaani Z, Al Kuwari E, Butt AA, Jeremijenko A, Kaleeckal AH, Latif AN, Shaik RM, Nasrallah GK, Benslimane FM, Al Khatib HA, Yassine HM, Al Kuwari MG, Al Romaihi HE, Al-Thani MH, Al Khal A, Bertollini R, Abu-Raddad LJ, Ayoub HH. Characterizing the effective reproduction number during the COVID-19 pandemic: Insights from Qatar’s experience. J Glob Health 2022; 12:05004. [PMID: 35136602 PMCID: PMC8819337 DOI: 10.7189/jogh.12.05004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Background The effective reproduction number, Rt, is a tool to track and understand pandemic dynamics. This investigation of Rt estimations was conducted to guide the national COVID-19 response in Qatar, from the onset of the pandemic until August 18, 2021. Methods Real-time “empirical” RtEmpirical was estimated using five methods, including the Robert Koch Institute, Cislaghi, Systrom-Bettencourt and Ribeiro, Wallinga and Teunis, and Cori et al. methods. Rt was also estimated using a transmission dynamics model (RtModel-based). Uncertainty and sensitivity analyses were conducted. Correlations between different Rt estimates were assessed by calculating correlation coefficients, and agreements between these estimates were assessed through Bland-Altman plots. Results RtEmpirical captured the evolution of the pandemic through three waves, public health response landmarks, effects of major social events, transient fluctuations coinciding with significant clusters of infection, and introduction and expansion of the Alpha (B.1.1.7) variant. The various estimation methods produced consistent and overall comparable RtEmpirical estimates with generally large correlation coefficients. The Wallinga and Teunis method was the fastest at detecting changes in pandemic dynamics. RtEmpirical estimates were consistent whether using time series of symptomatic PCR-confirmed cases, all PCR-confirmed cases, acute-care hospital admissions, or ICU-care hospital admissions, to proxy trends in true infection incidence. RtModel-based correlated strongly with RtEmpirical and provided an average RtEmpirical. Conclusions Rt estimations were robust and generated consistent results regardless of the data source or the method of estimation. Findings affirmed an influential role for Rt estimations in guiding national responses to the COVID-19 pandemic, even in resource-limited settings.
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Affiliation(s)
- Raghid Bsat
- Mathematics Program, Department of Mathematics, Statistics, and Physics, College of Arts and Sciences, Qatar University, Doha, Qatar
| | - Hiam Chemaitelly
- Infectious Disease Epidemiology Group, Weill Cornell Medicine-Qatar, Cornell University, Doha, Qatar
- World Health Organization Collaborating Centre for Disease Epidemiology Analytics on HIV/AIDS, Sexually Transmitted Infections, and Viral Hepatitis, Weill Cornell Medicine-Qatar, Cornell University, Qatar Foundation – Education City, Doha, Qatar
| | - Peter Coyle
- Hamad Medical Corporation, Doha, Qatar
- Biomedical Research Center, Member of QU Health, Qatar University, Doha, Qatar
- Wellcome-Wolfson Institute for Experimental Medicine, Queens University, Belfast, United Kingdom
| | - Patrick Tang
- Department of Pathology, Sidra Medicine, Doha, Qatar
| | | | | | | | - Adeel A Butt
- Hamad Medical Corporation, Doha, Qatar
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, New York, USA
| | | | | | | | | | - Gheyath K Nasrallah
- Biomedical Research Center, Member of QU Health, Qatar University, Doha, Qatar
- Department of Biomedical Science, College of Health Sciences, Member of QU Health, Qatar University, Doha, Qatar
| | - Fatiha M Benslimane
- Biomedical Research Center, Member of QU Health, Qatar University, Doha, Qatar
- Department of Biomedical Science, College of Health Sciences, Member of QU Health, Qatar University, Doha, Qatar
| | - Hebah A Al Khatib
- Biomedical Research Center, Member of QU Health, Qatar University, Doha, Qatar
- Department of Biomedical Science, College of Health Sciences, Member of QU Health, Qatar University, Doha, Qatar
| | - Hadi M Yassine
- Biomedical Research Center, Member of QU Health, Qatar University, Doha, Qatar
- Department of Biomedical Science, College of Health Sciences, Member of QU Health, Qatar University, Doha, Qatar
| | | | | | | | | | | | - Laith J Abu-Raddad
- Infectious Disease Epidemiology Group, Weill Cornell Medicine-Qatar, Cornell University, Doha, Qatar
- World Health Organization Collaborating Centre for Disease Epidemiology Analytics on HIV/AIDS, Sexually Transmitted Infections, and Viral Hepatitis, Weill Cornell Medicine-Qatar, Cornell University, Qatar Foundation – Education City, Doha, Qatar
- Department of Public Health, College of Health Sciences, Member of QU Health, Qatar University, Doha, Qatar
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, New York, USA
| | - Houssein H Ayoub
- Mathematics Program, Department of Mathematics, Statistics, and Physics, College of Arts and Sciences, Qatar University, Doha, Qatar
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159
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Yang X, Wang S, Xing Y, Li L, Xu RYD, Friston KJ, Guo Y. Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19. PLoS Comput Biol 2022; 18:e1009807. [PMID: 35196320 PMCID: PMC8923496 DOI: 10.1371/journal.pcbi.1009807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 03/15/2022] [Accepted: 01/05/2022] [Indexed: 11/18/2022] Open
Abstract
Estimating the changes of epidemiological parameters, such as instantaneous reproduction number, Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to estimate the instantaneous reproduction number Rt during emerging epidemics, resulting in the state-of-the-art 'DARt' system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt; the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in describing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for making accurate and timely estimation for transmission dynamics based on reported data.
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Affiliation(s)
- Xian Yang
- Department of Computer Science, Hong Kong Baptist University, Hong Kong Special Administrative Region, China
- Data Science Institute, Imperial College London, London, United Kingdom
| | - Shuo Wang
- Data Science Institute, Imperial College London, London, United Kingdom
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Shanghai, China
| | - Yuting Xing
- Data Science Institute, Imperial College London, London, United Kingdom
| | - Ling Li
- School of Computing, University of Kent, Kent, United Kingdom
| | - Richard Yi Da Xu
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia
| | - Karl J. Friston
- Institute of Neurology, University College London, London, United Kingdom
| | - Yike Guo
- Department of Computer Science, Hong Kong Baptist University, Hong Kong Special Administrative Region, China
- Data Science Institute, Imperial College London, London, United Kingdom
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160
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Svoboda J, Tkadlec J, Pavlogiannis A, Chatterjee K, Nowak MA. Infection dynamics of COVID-19 virus under lockdown and reopening. Sci Rep 2022; 12:1526. [PMID: 35087091 PMCID: PMC8795434 DOI: 10.1038/s41598-022-05333-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 01/05/2022] [Indexed: 01/08/2023] Open
Abstract
Motivated by COVID-19, we develop and analyze a simple stochastic model for the spread of disease in human population. We track how the number of infected and critically ill people develops over time in order to estimate the demand that is imposed on the hospital system. To keep this demand under control, we consider a class of simple policies for slowing down and reopening society and we compare their efficiency in mitigating the spread of the virus from several different points of view. We find that in order to avoid overwhelming of the hospital system, a policy must impose a harsh lockdown or it must react swiftly (or both). While reacting swiftly is universally beneficial, being harsh pays off only when the country is patient about reopening and when the neighboring countries coordinate their mitigation efforts. Our work highlights the importance of acting decisively when closing down and the importance of patience and coordination between neighboring countries when reopening.
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Affiliation(s)
| | - Josef Tkadlec
- Department of Mathematics, Harvard University, Cambridge, MA, 02138, USA
| | | | | | - Martin A Nowak
- Department of Mathematics, Harvard University, Cambridge, MA, 02138, USA.
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA.
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161
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Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031113] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
In this paper, we propose a model-based method for the reconstruction of not directly measured epidemiological data. To solve this task, we developed a generic optimization-based approach to compute unknown time-dependent quantities (such as states, inputs, and parameters) of discrete-time stochastic nonlinear models using a sequence of output measurements. The problem was reformulated as a stochastic nonlinear model predictive control computation, where the unknown inputs and parameters were searched as functions of the uncertain states, such that the model output followed the observations. The unknown data were approximated by Gaussian distributions. The predictive control problem was solved over a relatively long time window in three steps. First, we approximated the expected trajectories of the unknown quantities through a nonlinear deterministic problem. In the next step, we fixed the expected trajectories and computed the corresponding variances using closed-form expressions. Finally, the obtained mean and variance values were used as an initial guess to solve the stochastic problem. To reduce the estimated uncertainty of the computed states, a closed-loop input policy was considered during the optimization, where the state-dependent gain values were determined heuristically. The applicability of the approach is illustrated through the estimation of the epidemiological data of the COVID-19 pandemic in Hungary. To describe the epidemic spread, we used a slightly modified version of a previously published and validated compartmental model, in which the vaccination process was taken into account. The mean and the variance of the unknown data (e.g., the number of susceptible, infected, or recovered people) were estimated using only the daily number of hospitalized patients. The problem was reformulated as a finite-horizon predictive control problem, where the unknown time-dependent parameter, the daily transmission rate of the disease, was computed such that the expected value of the computed number of hospitalized patients fit the truly observed data as much as possible.
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162
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Estimation of the Instantaneous Reproduction Number and Its Confidence Interval for Modeling the COVID-19 Pandemic. MATHEMATICS 2022. [DOI: 10.3390/math10020287] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
In this paper, we derive an optimal model for calculating the instantaneous reproduction number, which is an important metric to help in controlling the evolution of epidemics. Our approach, within a frequentist framework, gave us the opportunity to calculate a more realistic confidence interval, a fundamental tool for a safe interpretation of the instantaneous reproduction number value, so that health and governmental people pay more attention to it. Our reasoning begins by decoupling the incidence data in mean and Gaussian noise by using practical series analysis techniques; then, we continue with a likely relationship between the present and past incidence data. Monte Carlo simulations and numerical integrations were conducted to complement the analytical proofs, and illustrations are provided for each stage of analysis to validate the analytical results. Finally, a real case study is discussed with the incidence data of the Republic of Panama regarding the COVID-19 pandemic. We have shown that, for the calculation of the confidence interval of the instantaneous reproduction number, it is essential to include all sources of variability, not only the Poissonian processes of the incidences. This proposal is delivered with analysis tools developed with Microsoft Excel.
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163
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Gopal R, Chandrasekar VK, Lakshmanan M. Analysis of the second wave of COVID-19 in India based on SEIR model. THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS 2022; 231:3453-3460. [PMID: 35039761 PMCID: PMC8756415 DOI: 10.1140/epjs/s11734-022-00426-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 12/18/2021] [Indexed: 06/14/2023]
Abstract
India was under a grave threat from the second wave of the COVID-19 pandemic particularly in the beginning of May 2021. The situation appeared rather gloomy as the number of infected individuals/active cases had increased alarmingly during the months of May and June 2021 compared to the first wave peak. Indian government/state governments have been implementing various control measures such as lockdowns, setting up new hospitals, and putting travel restrictions at various stages to lighten the virus spread from the initial outbreak of the pandemic. Recently, we have studied the susceptible-exposed-infectious-removed (SEIR) dynamic modeling of the epidemic evolution of COVID-19 in India with the help of appropriate parameters quantifying the various governmental actions and the intensity of individual reactions. Our analysis had predicted the scenario of the first wave quite well. In this present article, we extend our analysis to estimate and analyze the number of infected individuals during the second wave of COVID-19 in India with the help of the above SEIR model. Our findings show that the people's individual effort along with governmental actions such as implementations of curfews and accelerated vaccine strategy are the most important factors to control the pandemic in the present situation and in the future.
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Affiliation(s)
- R Gopal
- Department of Physics, Centre for Nonlinear Science and Engineering, School of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur, 613 401 India
| | - V K Chandrasekar
- Department of Physics, Centre for Nonlinear Science and Engineering, School of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur, 613 401 India
| | - M Lakshmanan
- Department of Nonlinear Dynamics, School of Physics, Bharathidasan University, Tiruchirappalli, 620 014 Tamil Nadu India
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A COVINDEX based on a GAM beta regression model with an application to the COVID-19 pandemic in Italy. STAT METHOD APPL-GER 2022; 31:881-900. [PMID: 35035344 PMCID: PMC8743080 DOI: 10.1007/s10260-021-00617-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/25/2021] [Indexed: 11/04/2022]
Abstract
Detecting changes in COVID-19 disease transmission over time is a key indicator of epidemic growth. Near real-time monitoring of the pandemic growth is crucial for policy makers and public health officials who need to make informed decisions about whether to enforce lockdowns or allow certain activities. The effective reproduction number \documentclass[12pt]{minimal}
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\begin{document}$$R_t$$\end{document}Rt is the standard index used in many countries for this goal. However, it is known that due to the delays between infection and case registration, its use for decision making is somewhat limited. In this paper a near real-time COVINDEX is proposed for monitoring the evolution of the pandemic. The index is computed from predictions obtained from a GAM beta regression for modelling the test positive rate as a function of time. The proposal is illustrated using data on COVID-19 pandemic in Italy and compared with \documentclass[12pt]{minimal}
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\begin{document}$$R_t$$\end{document}Rt. A simple chart is also proposed for monitoring local and national outbreaks by policy makers and public health officials.
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165
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Chapman LAC, Barnard RC, Russell TW, Abbott S, van Zandvoort K, Davies NG, Kucharski AJ. Unexposed populations and potential COVID-19 hospitalisations and deaths in European countries as per data up to 21 November 2021. Euro Surveill 2022; 27:2101038. [PMID: 34991776 PMCID: PMC8739340 DOI: 10.2807/1560-7917.es.2022.27.1.2101038] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 12/16/2021] [Indexed: 02/02/2023] Open
Abstract
We estimate the potential remaining COVID-19 hospitalisation and death burdens in 19 European countries by estimating the proportion of each country's population that has acquired immunity to severe disease through infection or vaccination. Our results suggest many European countries could still face high burdens of hospitalisations and deaths, particularly those with lower vaccination coverage, less historical transmission and/or older populations. Continued non-pharmaceutical interventions and efforts to achieve high vaccination coverage are required in these countries to limit severe COVID-19 outcomes.
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Affiliation(s)
- Lloyd A C Chapman
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Rosanna C Barnard
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Timothy W Russell
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Sam Abbott
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Kevin van Zandvoort
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Nicholas G Davies
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Adam J Kucharski
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
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166
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Ong J, Soh S, Ho SH, Seah A, Dickens BS, Tan KW, Koo JR, Cook AR, Richards DR, Gaw LYF, Ng LC, Lim JT. Fine-scale estimation of effective reproduction numbers for dengue surveillance. PLoS Comput Biol 2022; 18:e1009791. [PMID: 35051176 PMCID: PMC8836367 DOI: 10.1371/journal.pcbi.1009791] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 02/11/2022] [Accepted: 12/29/2021] [Indexed: 12/25/2022] Open
Abstract
The effective reproduction number Rt is an epidemiological quantity that provides an instantaneous measure of transmission potential of an infectious disease. While dengue is an increasingly important vector-borne disease, few have used Rt as a measure to inform public health operations and policy for dengue. This study demonstrates the utility of Rt for real time dengue surveillance. Using nationally representative, geo-located dengue case data from Singapore over 2010–2020, we estimated Rt by modifying methods from Bayesian (EpiEstim) and filtering (EpiFilter) approaches, at both the national and local levels. We conducted model assessment of Rt from each proposed method and determined exogenous temporal and spatial drivers for Rt in relation to a wide range of environmental and anthropogenic factors. At the national level, both methods achieved satisfactory model performance (R2EpiEstim = 0.95, R2EpiFilter = 0.97), but disparities in performance were large at finer spatial scales when case counts are low (MASE EpiEstim = 1.23, MASEEpiFilter = 0.59). Impervious surfaces and vegetation with structure dominated by human management (without tree canopy) were positively associated with increased transmission intensity. Vegetation with structure dominated by human management (with tree canopy), on the other hand, was associated with lower dengue transmission intensity. We showed that dengue outbreaks were preceded by sustained periods of high transmissibility, demonstrating the potential of Rt as a dengue surveillance tool for detecting large rises in dengue cases. Real time estimation of Rt at the fine scale can assist public health agencies in identifying high transmission risk areas and facilitating localised outbreak preparedness and response. The effective reproduction number Rt is an epidemiological quantity that provides an instantaneous measure of transmission potential of an infectious disease. While dengue is an increasingly important vector-borne disease, few have used Rt as a measure to inform public health operations and policy for dengue. This study demonstrates the utility of Rt for real time dengue surveillance. Using nationally representative, geo-located dengue case data from Singapore over 2010–2020, we estimated Rt by modifying methods from Bayesian (EpiEstim) and filtering (EpiFilter) approaches, at both the national and local levels. We conducted model assessment of Rt from each proposed method and determined exogenous temporal and spatial drivers for Rt in relation to a wide range of environmental and anthropogenic factors. At the national level, both methods achieved high accuracy, but disparities in performance were large at finer spatial scales when case counts are low. This study demonstrates the potential of Rt as a dengue surveillance tool for detecting large rises in dengue cases. Real time estimation of Rt at the fine scale can assist public health agencies in identifying high transmission risk areas and facilitating localised outbreak preparedness and response.
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Affiliation(s)
- Janet Ong
- Environmental Health Institute, National Environment Agency, Singapore
| | - Stacy Soh
- Environmental Health Institute, National Environment Agency, Singapore
| | - Soon Hoe Ho
- Environmental Health Institute, National Environment Agency, Singapore
| | - Annabel Seah
- Environmental Health Institute, National Environment Agency, Singapore
| | - Borame Sue Dickens
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Ken Wei Tan
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Joel Ruihan Koo
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Alex R. Cook
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | | | - Leon Yan-Feng Gaw
- School of Design and Environment, National University of Singapore, Singapore
| | - Lee Ching Ng
- Environmental Health Institute, National Environment Agency, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore
| | - Jue Tao Lim
- Environmental Health Institute, National Environment Agency, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
- * E-mail:
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167
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Markwalter CF, Menya D, Wesolowski A, Esimit D, Lokoel G, Kipkoech J, Freedman E, Sumner KM, Abel L, Ambani G, Meredith HR, Taylor SM, Obala AA, O'Meara WP. Plasmodium falciparum importation does not sustain malaria transmission in a semi-arid region of Kenya. PLOS GLOBAL PUBLIC HEALTH 2022; 2:e0000807. [PMID: 36962553 PMCID: PMC10021402 DOI: 10.1371/journal.pgph.0000807] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 07/17/2022] [Indexed: 11/19/2022]
Abstract
Human movement impacts the spread and transmission of infectious diseases. Recently, a large reservoir of Plasmodium falciparum malaria was identified in a semi-arid region of northwestern Kenya historically considered unsuitable for malaria transmission. Understanding the sources and patterns of transmission attributable to human movement would aid in designing and targeting interventions to decrease the unexpectedly high malaria burden in the region. Toward this goal, polymorphic parasite genes (ama1, csp) in residents and passengers traveling to Central Turkana were genotyped by amplicon deep sequencing. Genotyping and epidemiological data were combined to assess parasite importation. The contribution of travel to malaria transmission was estimated by modelling case reproductive numbers inclusive and exclusive of travelers. P. falciparum was detected in 6.7% (127/1891) of inbound passengers, including new haplotypes which were later detected in locally-transmitted infections. Case reproductive numbers approximated 1 and did not change when travelers were removed from transmission networks, suggesting that transmission is not fueled by travel to the region but locally endemic. Thus, malaria is not only prevalent in Central Turkana but also sustained by local transmission. As such, interrupting importation is unlikely to be an effective malaria control strategy on its own, but targeting interventions locally has the potential to drive down transmission.
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Affiliation(s)
| | - Diana Menya
- School of Public Health, Moi University College of Health Sciences, Eldoret, Kenya
| | - Amy Wesolowski
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Daniel Esimit
- Department of Health Services and Sanitation, Turkana County, Kenya
| | - Gilchrist Lokoel
- Department of Health Services and Sanitation, Turkana County, Kenya
| | - Joseph Kipkoech
- Academic Model Providing Access to Healthcare, Eldoret, Kenya
| | - Elizabeth Freedman
- Duke University School of Medicine, Durham, North Carolina, United States of America
| | - Kelsey M Sumner
- Duke University School of Medicine, Durham, North Carolina, United States of America
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Lucy Abel
- Academic Model Providing Access to Healthcare, Eldoret, Kenya
| | - George Ambani
- Academic Model Providing Access to Healthcare, Eldoret, Kenya
| | - Hannah R Meredith
- Duke Global Health Institute, Durham, North Carolina, United States of America
| | - Steve M Taylor
- Duke Global Health Institute, Durham, North Carolina, United States of America
- Duke University School of Medicine, Durham, North Carolina, United States of America
| | - Andrew A Obala
- School of Medicine, Moi University College of Health Sciences, Eldoret, Kenya
| | - Wendy P O'Meara
- Duke Global Health Institute, Durham, North Carolina, United States of America
- Duke University School of Medicine, Durham, North Carolina, United States of America
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168
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Politis MD, Hua X, Ogwara CA, Davies MR, Adebile TM, Sherman MP, Zhou X, Chowell G, Spaulding AC, Fung ICH. nSpatially refined time-varying reproduction numbers of SARS-CoV-2 in Arkansas and Kentucky and their relationship to population size and public health policy, March – November, 2020. Ann Epidemiol 2022; 68:37-44. [PMID: 35031444 PMCID: PMC8750695 DOI: 10.1016/j.annepidem.2021.12.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 12/17/2021] [Accepted: 12/22/2021] [Indexed: 11/25/2022]
Abstract
Purpose Methods Results Conclusions
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169
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Mee P, Alexander N, Mayaud P, González FDJC, Abbott S, Santos AADS, Acosta AL, Parag KV, Pereira RH, Prete CA, Sabino EC, Faria NR, Brady OJ. Tracking the emergence of disparities in the subnational spread of COVID-19 in Brazil using an online application for real-time data visualisation: A longitudinal analysis. LANCET REGIONAL HEALTH. AMERICAS 2022; 5:None. [PMID: 35098203 PMCID: PMC8782271 DOI: 10.1016/j.lana.2021.100119] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
BACKGROUND Brazil is one of the countries worst affected by the COVID-19 pandemic with over 20 million cases and 557,000 deaths reported by August 2021. Comparison of real-time local COVID-19 data between areas is essential for understanding transmission, measuring the effects of interventions, and predicting the course of the epidemic, but are often challenging due to different population sizes and structures. METHODS We describe the development of a new app for the real-time visualisation of COVID-19 data in Brazil at the municipality level. In the CLIC-Brazil app, daily updates of case and death data are downloaded, age standardised and used to estimate the effective reproduction number (Rt ). We show how such platforms can perform real-time regression analyses to identify factors associated with the rate of initial spread and early reproduction number. We also use survival methods to predict the likelihood of occurrence of a new peak of COVID-19 incidence. FINDINGS After an initial introduction in São Paulo and Rio de Janeiro states in early March 2020, the epidemic spread to northern states and then to highly populated coastal regions and the Central-West. Municipalities with higher metrics of social development experienced earlier arrival of COVID-19 (decrease of 11·1 days [95% CI:8.9,13.2] in the time to arrival for each 10% increase in the social development index). Differences in the initial epidemic intensity (mean Rt ) were largely driven by geographic location and the date of local onset. INTERPRETATION This study demonstrates that platforms that monitor, standardise and analyse the epidemiological data at a local level can give useful real-time insights into outbreak dynamics that can be used to better adapt responses to the current and future pandemics. FUNDING This project was supported by a Medical Research Council UK (MRC-UK) -São Paulo Research Foundation (FAPESP) CADDE partnership award (MR/S0195/1 and FAPESP 18/14389-0).
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Affiliation(s)
- Paul Mee
- Faculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- International Statistics and Epidemiology Group, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Neal Alexander
- Faculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
- International Statistics and Epidemiology Group, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Philippe Mayaud
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Felipe de Jesus Colón González
- Faculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Sam Abbott
- Faculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | - André Luís Acosta
- Instituto de Biociências, Universidade de São Paulo, São Paulo, Brazil
| | - Kris V. Parag
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Imperial College London, London, United Kingdom
| | | | - Carlos A. Prete
- Department of Electronic Systems Engineering, University of São Paulo, São Paulo, Brazil
| | - Ester C. Sabino
- Departamento de Molestias Infecciosas e Parasitarias and Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, Brazil
| | - Nuno R. Faria
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Imperial College London, London, United Kingdom
- Departamento de Molestias Infecciosas e Parasitarias and Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, Brazil
- Department of Zoology, University of Oxford, Oxford, United Kingdom
| | | | - Oliver J Brady
- Faculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
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170
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Hridoy AEE, Tipo IH, Sami MS, Babu MR, Ahmed MS, Rahman SM, Tusher SMSH, Rashid KJ, Naim M. Spatio-temporal estimation of basic and effective reproduction number of COVID-19 and post-lockdown transmissibility in Bangladesh. SPATIAL INFORMATION RESEARCH 2022; 30:23-35. [PMCID: PMC8237036 DOI: 10.1007/s41324-021-00409-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 05/28/2021] [Accepted: 06/04/2021] [Indexed: 11/04/2023]
Abstract
The ongoing COVID-19 pandemic has caused unprecedented public health concern in Bangladesh. This study investigated the role of Non-Pharmaceutical Interventions on COVID-19 transmission and post-lockdown scenarios of 64 administrative districts and the country as a whole based on the spatiotemporal variations of effective reproduction number (R t) of COVID-19 incidences. The daily confirmed COVID-19 data of Bangladesh and its administrative districts from March 8, 2020, to March 10, 2021, were used to estimate R t. This study finds that the maximum value of R t reached 4.15 (3.43, 4.97, 95% CI) in late March 2020, which remained above 1 afterwards in most of the districts. Containment measures are moderately effective in reducing transmission by 24.03%. The R t was established below 1 from early December 2020 for overall Bangladesh and a gradual increase of R t above 1 has been seen from early February 2021. The basic reproduction number (R 0) in Bangladesh probably varied around 2.02 (1.33–3.28, 95% CI). This study finds a significant positive correlation (r = 0.75) between population density and COVID-19 incidence and explaining 56% variation in Bangladesh. The findings of this study are expected to support the policymakers to adopt appropriate measures for curbing the COVID-19 transmission effectively.
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Affiliation(s)
- Al-Ekram Elahee Hridoy
- Department of Geography and Environmental Studies, University of Chittagong, Chattogram, 4331 Bangladesh
| | - Imrul Hasan Tipo
- Department of Biochemistry and Molecular Biology, University of Chittagong, Chattogram, 4331 Bangladesh
| | - Md. Shamsudduha Sami
- Department of Geography and Environment, Jagannath University, Dhaka, 1100 Bangladesh
| | - Md. Ripon Babu
- Department of Biochemistry and Molecular Biology, University of Chittagong, Chattogram, 4331 Bangladesh
| | - Md. Sayem Ahmed
- Department of Pharmacy, East West University, Dhaka, 1212 Bangladesh
| | - Syed Masiur Rahman
- Center for Environment & Water, Research Institute, King Fahd University of Petroleum & Minerals, KFUPM Box 713, Dhahran, 31261 Saudi Arabia
| | | | - Kazi Jihadur Rashid
- Center for Environmental and Geographic Information Services (CEGIS), Dhaka, 1212 Bangladesh
| | - Mohammad Naim
- Department of Electrical and Computer Engineering, North South University, Dhaka, 1229 Bangladesh
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171
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Kishore N, Taylor AR, Jacob PE, Vembar N, Cohen T, Buckee CO, Menzies NA. Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study. Lancet Digit Health 2022; 4:e27-e36. [PMID: 34740555 PMCID: PMC8563007 DOI: 10.1016/s2589-7500(21)00214-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 07/22/2021] [Accepted: 09/01/2021] [Indexed: 01/05/2023]
Abstract
BACKGROUND In early 2020, the response to the SARS-CoV-2 pandemic focused on non-pharmaceutical interventions, some of which aimed to reduce transmission by changing mixing patterns between people. Aggregated location data from mobile phones are an important source of real-time information about human mobility on a population level, but the degree to which these mobility metrics capture the relevant contact patterns of individuals at risk of transmitting SARS-CoV-2 is not clear. In this study we describe changes in the relationship between mobile phone data and SARS-CoV-2 transmission in the USA. METHODS In this population-based study, we collected epidemiological data on COVID-19 cases and deaths, as well as human mobility metrics collated by advertisement technology that was derived from global positioning systems, from 1396 counties across the USA that had at least 100 laboratory-confirmed cases of COVID-19. We grouped these counties into six ordinal categories, defined by the National Center for Health Statistics (NCHS) and graded from urban to rural, and quantified the changes in COVID-19 transmission using estimates of the effective reproduction number (Rt) between Jan 22 and July 9, 2020, to investigate the relationship between aggregated mobility metrics and epidemic trajectory. For each county, we model the time series of Rt values with mobility proxies. FINDINGS We show that the reproduction number is most strongly associated with mobility proxies for change in the travel into counties (0·757 [95% CI 0·689 to 0·857]), but this relationship primarily holds for counties in the three most urban categories as defined by the NCHS. This relationship weakens considerably after the initial 15 weeks of the epidemic (0·442 [-0·492 to -0·392]), consistent with the emergence of more complex local policies and behaviours, including masking. INTERPRETATION Our study shows that the integration of mobility metrics into retrospective modelling efforts can be useful in identifying links between these metrics and Rt. Importantly, we highlight potential issues in the data generation process for transmission indicators derived from mobile phone data, representativeness, and equity of access, which must be addressed to improve the interpretability of these data in public health. FUNDING There was no funding source for this study.
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Affiliation(s)
- Nishant Kishore
- Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Aimee R Taylor
- Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard T H Chan School of Public Health, Boston, MA, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Pierre E Jacob
- Department of Information Systems, Decision Sciences and Statistics, ESSEC Business School, Cergy, France
| | | | - Ted Cohen
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Caroline O Buckee
- Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard T H Chan School of Public Health, Boston, MA, USA.
| | - Nicolas A Menzies
- Department of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA
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172
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Markov PV, Katzourakis A, Stilianakis NI. Antigenic evolution will lead to new SARS-CoV-2 variants with unpredictable severity. Nat Rev Microbiol 2022; 20:251-252. [PMID: 35288685 PMCID: PMC8919145 DOI: 10.1038/s41579-022-00722-z] [Citation(s) in RCA: 72] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Peter V. Markov
- grid.434554.70000 0004 1758 4137European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Aris Katzourakis
- grid.4991.50000 0004 1936 8948Department of Zoology, University of Oxford, Oxford, UK
| | - Nikolaos I. Stilianakis
- grid.434554.70000 0004 1758 4137European Commission, Joint Research Centre (JRC), Ispra, Italy ,grid.5330.50000 0001 2107 3311Department of Biometry and Epidemiology, University of Erlangen-Nuremberg, Erlangen, Germany
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173
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Jewell NP. Statistical Models for COVID-19 Incidence, Cumulative Prevalence, and R t. J Am Stat Assoc 2021; 116:1578-1582. [DOI: 10.1080/01621459.2021.1983436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Nicholas P. Jewell
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
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174
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Quick C, Dey R, Lin X. Regression Models for Understanding COVID-19 Epidemic Dynamics With Incomplete Data. J Am Stat Assoc 2021; 116:1561-1577. [DOI: 10.1080/01621459.2021.2001339] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Corbin Quick
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Rounak Dey
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Statistics, Faculty of Arts and Sciences, Harvard University, Cambridge, MA
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175
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Kirkegaard JB, Sneppen K. Superspreading quantified from bursty epidemic trajectories. Sci Rep 2021; 11:24124. [PMID: 34916534 PMCID: PMC8677763 DOI: 10.1038/s41598-021-03126-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 11/26/2021] [Indexed: 11/20/2022] Open
Abstract
The quantification of spreading heterogeneity in the COVID-19 epidemic is crucial as it affects the choice of efficient mitigating strategies irrespective of whether its origin is biological or social. We present a method to deduce temporal and individual variations in the basic reproduction number directly from epidemic trajectories at a community level. Using epidemic data from the 98 districts in Denmark we estimate an overdispersion factor k for COVID-19 to be about 0.11 (95% confidence interval 0.08-0.18), implying that 10 % of the infected cause between 70 % and 87 % of all infections.
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Affiliation(s)
| | - Kim Sneppen
- Niels Bohr Institute, University of Copenhagen, 2100, Copenhagen, Denmark
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176
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Epting ME, Pluznik JA, Levano SR, Hua X, Fung ICH, Jordan B, O'Donovan E, Robinson KM, Chakraborty R, Yousefi B, Michel CJ, Bowden CJ, Kapadia A, Riback LR, Mangla AT, Akiyama MJ, Spaulding AC. Aiming for Zero: Reducing Transmission of Coronavirus Disease 2019 in the D.C. Department of Corrections. Open Forum Infect Dis 2021; 8:ofab547. [PMID: 34901301 PMCID: PMC8661082 DOI: 10.1093/ofid/ofab547] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 11/05/2021] [Indexed: 12/11/2022] Open
Abstract
Background Washington, District of Columbia lowered severe acute respiratory syndrome coronavirus 2 transmission in its large jail while community incidence was still high Methods Coordinated clinical and operational interventions brought new cases to near zero. Results Aggressive infection control and underlying jail architecture can promote correctional coronavirus disease 2019 management. Conclusions More intensive monitoring could help confirm that in-house transmission is truly zero.
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Affiliation(s)
- Mallory E Epting
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Jacob A Pluznik
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Samantha R Levano
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Xinyi Hua
- Department of Biostatistics, Epidemiology, and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia, USA
| | - Isaac C H Fung
- Department of Biostatistics, Epidemiology, and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia, USA
| | - Beth Jordan
- Washington DC Department of Corrections, Washington, District of Columbia, USA
| | | | | | - Reena Chakraborty
- Washington DC Department of Corrections, Washington, District of Columbia, USA
| | - Bahram Yousefi
- Washington DC Department of Corrections, Washington, District of Columbia, USA
| | - Ciara J Michel
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Chava J Bowden
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Aman Kapadia
- Unity Healthcare, Washington, District of Columbia, USA
| | - Lindsey R Riback
- Department of Medicine, Montefiore Hospital, New York City, New York, USA
| | - Anil T Mangla
- Washington DC Department of Health, Washington, District of Columbia, USA
| | - Matthew J Akiyama
- Department of Medicine, Montefiore Hospital, New York City, New York, USA
| | - Anne C Spaulding
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
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177
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Alvarez L, Colom M, Morel JD, Morel JM. Computing the daily reproduction number of COVID-19 by inverting the renewal equation using a variational technique. Proc Natl Acad Sci U S A 2021; 118:e2105112118. [PMID: 34876517 PMCID: PMC8685677 DOI: 10.1073/pnas.2105112118] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/11/2021] [Indexed: 12/20/2022] Open
Abstract
The COVID-19 pandemic has undergone frequent and rapid changes in its local and global infection rates, driven by governmental measures or the emergence of new viral variants. The reproduction number Rt indicates the average number of cases generated by an infected person at time t and is a key indicator of the spread of an epidemic. A timely estimation of Rt is a crucial tool to enable governmental organizations to adapt quickly to these changes and assess the consequences of their policies. The EpiEstim method is the most widely accepted method for estimating Rt But it estimates Rt with a significant temporal delay. Here, we propose a method, EpiInvert, that shows good agreement with EpiEstim, but that provides estimates of Rt several days in advance. We show that Rt can be estimated by inverting the renewal equation linking Rt with the observed incidence curve of new cases, it Our signal-processing approach to this problem yields both Rt and a restored it corrected for the "weekend effect" by applying a deconvolution and denoising procedure. The implementations of the EpiInvert and EpiEstim methods are fully open source and can be run in real time on every country in the world and every US state.
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Affiliation(s)
- Luis Alvarez
- Centro de Tecnologías de la Imagen, Departamento de Informática y Sistemas, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain;
| | - Miguel Colom
- Centre Borelli, Université Paris-Saclay, École Normale Supérieure Paris-Saclay, CNRS, F-91190 Gif-sur-Yvette, France
| | - Jean-David Morel
- Laboratoire de Physiologie Intégrative et Systémique, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Jean-Michel Morel
- Centre Borelli, Université Paris-Saclay, École Normale Supérieure Paris-Saclay, CNRS, F-91190 Gif-sur-Yvette, France
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178
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Alvarez L, Colom M, Morel JD, Morel JM. Computing the daily reproduction number of COVID-19 by inverting the renewal equation using a variational technique. Proc Natl Acad Sci U S A 2021. [PMID: 34876517 DOI: 10.1073/pnas.210511211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023] Open
Abstract
The COVID-19 pandemic has undergone frequent and rapid changes in its local and global infection rates, driven by governmental measures or the emergence of new viral variants. The reproduction number Rt indicates the average number of cases generated by an infected person at time t and is a key indicator of the spread of an epidemic. A timely estimation of Rt is a crucial tool to enable governmental organizations to adapt quickly to these changes and assess the consequences of their policies. The EpiEstim method is the most widely accepted method for estimating Rt But it estimates Rt with a significant temporal delay. Here, we propose a method, EpiInvert, that shows good agreement with EpiEstim, but that provides estimates of Rt several days in advance. We show that Rt can be estimated by inverting the renewal equation linking Rt with the observed incidence curve of new cases, it Our signal-processing approach to this problem yields both Rt and a restored it corrected for the "weekend effect" by applying a deconvolution and denoising procedure. The implementations of the EpiInvert and EpiEstim methods are fully open source and can be run in real time on every country in the world and every US state.
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Affiliation(s)
- Luis Alvarez
- Centro de Tecnologías de la Imagen, Departamento de Informática y Sistemas, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain;
| | - Miguel Colom
- Centre Borelli, Université Paris-Saclay, École Normale Supérieure Paris-Saclay, CNRS, F-91190 Gif-sur-Yvette, France
| | - Jean-David Morel
- Laboratoire de Physiologie Intégrative et Systémique, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Jean-Michel Morel
- Centre Borelli, Université Paris-Saclay, École Normale Supérieure Paris-Saclay, CNRS, F-91190 Gif-sur-Yvette, France
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179
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Gostic KM, McGough L, Baskerville EB, Abbott S, Joshi K, Tedijanto C, Kahn R, Niehus R, Hay JA, De Salazar PM, Hellewell J, Meakin S, Munday JD, Bosse NI, Sherrat K, Thompson RN, White LF, Huisman JS, Scire J, Bonhoeffer S, Stadler T, Wallinga J, Funk S, Lipsitch M, Cobey S. Correction: Practical considerations for measuring the effective reproductive number, Rt. PLoS Comput Biol 2021; 17:e1009679. [PMID: 34879070 PMCID: PMC8654153 DOI: 10.1371/journal.pcbi.1009679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
[This corrects the article DOI: 10.1371/journal.pcbi.1008409.].
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180
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Vanni F, Lambert D, Palatella L, Grigolini P. On the use of aggregated human mobility data to estimate the reproduction number. Sci Rep 2021; 11:23286. [PMID: 34857840 PMCID: PMC8639785 DOI: 10.1038/s41598-021-02760-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 11/19/2021] [Indexed: 11/09/2022] Open
Abstract
The reproduction number of an infectious disease, such as CoViD-19, can be described through a modified version of the susceptible-infected-recovered (SIR) model with time-dependent contact rate, where mobility data are used as proxy of average movement trends and interpersonal distances. We introduce a theoretical framework to explain and predict changes in the reproduction number of SARS-CoV-2 in terms of aggregated individual mobility and interpersonal proximity (alongside other epidemiological and environmental variables) during and after the lockdown period. We use an infection-age structured model described by a renewal equation. The model predicts the evolution of the reproduction number up to a week ahead of well-established estimates used in the literature. We show how lockdown policies, via reduction of proximity and mobility, reduce the impact of CoViD-19 and mitigate the risk of disease resurgence. We validate our theoretical framework using data from Google, Voxel51, Unacast, The CoViD-19 Mobility Data Network, and Analisi Distribuzione Aiuti.
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Affiliation(s)
- Fabio Vanni
- CNRS, GREDEG, Université Côte d'Azur, Nice, France. .,Sciences Po, OFCE, Campus de Sophia Antipolis, Nice, France. .,Center for Nonlinear Science, University of North Texas, Denton, TX, USA.
| | - David Lambert
- Center for Nonlinear Science, University of North Texas, Denton, TX, USA.,Department of Mathematics, University of North Texas, Denton, TX, USA
| | | | - Paolo Grigolini
- Center for Nonlinear Science, University of North Texas, Denton, TX, USA
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181
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Jung SM, Endo A, Akhmetzhanov AR, Nishiura H. Predicting the effective reproduction number of COVID-19: inference using human mobility, temperature, and risk awareness. Int J Infect Dis 2021; 113:47-54. [PMID: 34628020 PMCID: PMC8498007 DOI: 10.1016/j.ijid.2021.10.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 09/29/2021] [Accepted: 10/02/2021] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVES The effective reproduction number (Rt) has been critical for assessing the effectiveness of countermeasures during the coronavirus disease 2019 (COVID-19) pandemic. Conventional methods using reported incidences are unable to provide timely Rt data due to the delay from infection to reporting. Our study aimed to develop a framework for predicting Rt in real time, using timely accessible data - i.e. human mobility, temperature, and risk awareness. METHODS A linear regression model to predict Rt was designed and embedded in the renewal process. Four prefectures of Japan with high incidences in the first wave were selected for model fitting and validation. Predictive performance was assessed by comparing the observed and predicted incidences using cross-validation, and by testing on a separate dataset in two other prefectures with distinct geographical settings from the four studied prefectures. RESULTS The predicted mean values of Rt and 95% uncertainty intervals followed the overall trends for incidence, while predictive performance was diminished when Rt changed abruptly, potentially due to superspreading events or when stringent countermeasures were implemented. CONCLUSIONS The described model can potentially be used for monitoring the transmission dynamics of COVID-19 ahead of the formal estimates, subject to delay, providing essential information for timely planning and assessment of countermeasures.
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Affiliation(s)
- Sung-mok Jung
- Kyoto University School of Public Health, Yoshidakonoe cho, Sakyo ku, Kyoto city, 60-68501, Japan,Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo-shi, Hokkaido 060-8638, Japan
| | - Akira Endo
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Andrei R. Akhmetzhanov
- National Taiwan University College of Public Health, 17 Xu-Zhou Road, Taipei, 10055, Taiwan
| | - Hiroshi Nishiura
- Kyoto University School of Public Health, Yoshidakonoe cho, Sakyo ku, Kyoto city, 60-68501, Japan.
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182
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Jayaraj VJ, Rampal S, Ng CW, Chong DWQ. The Epidemiology of COVID-19 in Malaysia. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2021; 17:100295. [PMID: 34704083 PMCID: PMC8529946 DOI: 10.1016/j.lanwpc.2021.100295] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/13/2021] [Accepted: 09/16/2021] [Indexed: 01/02/2023]
Abstract
BACKGROUND COVID-19 has rapidly spread across the globe. Critical to the control of COVID-19 is the characterisation of its epidemiology. Despite this, there has been a paucity of evidence from many parts of the world, including Malaysia. We aim to describe the epidemiology of COVID-19 in Malaysia to inform prevention and control policies better. METHODS Malaysian COVID-19 data was extracted from 16 March 2020 up to 31 May 2021. We estimated the following epidemiological indicators: 7-day incidence rates, 7-day mortality rates, case fatality rates, test positive ratios, testing rates and the time-varying reproduction number (Rt). FINDINGS Between 16 March 2020 and 31 May 2021, Malaysia has reported 571,901 cases and 2,796 deaths. Malaysia's average 7-day incidence rate was 26•6 reported infections per 100,000 population (95% CI: 17•8, 38•1). The average test positive ratio and testing rate were 4•3% (95% CI: 1•6, 10•2) and 0•8 tests per 1,000 population (95% CI: <0•1, 3•7), respectively. The case fatality rates (CFR) was 0•6% (95% CI: <0•1, 3•7). Among the 2,796 cases who died, 87•3% were ≥ 50 years. INTERPRETATION The public health response was successful in the suppression of COVID-19 transmission or the first half of 2020. However, a state election and outbreaks in institutionalised populations have been the catalyst for more significant community propagation. This rising community transmission has continued in 2021, leading to increased incidence and strained healthcare systems. Calibrating NPI based on epidemiological indicators remain critical for us to live with the virus. (243 words). FUNDING This study is part of the COVID-19 Epidemiological Analysis and Strategies (CEASe) Project with funding from the Ministry of Science, Technology and Innovation (UM.0000245/HGA.GV).
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Affiliation(s)
- Vivek Jason Jayaraj
- Centre for Epidemiology and Evidence-based Practice, Department of Social and Preventive, Medicine, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
- Ministry of Health Malaysia, Putrajaya, Malaysia
| | - Sanjay Rampal
- Centre for Epidemiology and Evidence-based Practice, Department of Social and Preventive, Medicine, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Chiu-Wan Ng
- Centre for Epidemiology and Evidence-based Practice, Department of Social and Preventive, Medicine, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Diane Woei Quan Chong
- Centre for Epidemiology and Evidence-based Practice, Department of Social and Preventive, Medicine, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
- Ministry of Health Malaysia, Putrajaya, Malaysia
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183
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Andriamandimby SF, Brook CE, Razanajatovo N, Randriambolamanantsoa TH, Rakotondramanga JM, Rasambainarivo F, Raharimanga V, Razanajatovo IM, Mangahasimbola R, Razafindratsimandresy R, Randrianarisoa S, Bernardson B, Rabarison JH, Randrianarisoa M, Nasolo FS, Rabetombosoa RM, Ratsimbazafy AM, Raharinosy V, Rabemananjara AH, Ranaivoson CH, Razafimanjato H, Randremanana R, Héraud JM, Dussart P. Cross-sectional cycle threshold values reflect epidemic dynamics of COVID-19 in Madagascar. Epidemics 2021; 38:100533. [PMID: 34896895 PMCID: PMC8628610 DOI: 10.1016/j.epidem.2021.100533] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 09/23/2021] [Accepted: 11/27/2021] [Indexed: 01/12/2023] Open
Abstract
As the national reference laboratory for febrile illness in Madagascar, we processed samples from the first epidemic wave of COVID-19, between March and September 2020. We fit generalized additive models to cycle threshold (Ct) value data from our RT-qPCR platform, demonstrating a peak in high viral load, low-Ct value infections temporally coincident with peak epidemic growth rates estimated in real time from publicly-reported incidence data and retrospectively from our own laboratory testing data across three administrative regions. We additionally demonstrate a statistically significant effect of duration of time since infection onset on Ct value, suggesting that Ct value can be used as a biomarker of the stage at which an individual is sampled in the course of an infection trajectory. As an extension, the population-level Ct distribution at a given timepoint can be used to estimate population-level epidemiological dynamics. We illustrate this concept by adopting a recently-developed, nested modeling approach, embedding a within-host viral kinetics model within a population-level Susceptible-Exposed-Infectious-Recovered (SEIR) framework, to mechanistically estimate epidemic growth rates from cross-sectional Ct distributions across three regions in Madagascar. We find that Ct-derived epidemic growth estimates slightly precede those derived from incidence data across the first epidemic wave, suggesting delays in surveillance and case reporting. Our findings indicate that public reporting of Ct values could offer an important resource for epidemiological inference in low surveillance settings, enabling forecasts of impending incidence peaks in regions with limited case reporting.
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Affiliation(s)
| | - Cara E Brook
- Department of Ecology and Evolution, University of Chicago, United States
| | | | | | | | | | | | | | | | | | | | - Barivola Bernardson
- Epidemiology and Clinical Research Unit, Institut Pasteur de Madagascar, Madagascar
| | | | | | | | | | | | | | | | | | | | - Rindra Randremanana
- Virology Unit, Institut Pasteur de Madagascar, Madagascar; Epidemiology and Clinical Research Unit, Institut Pasteur de Madagascar, Madagascar
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184
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Lau YC, Tsang TK, Kennedy-Shaffer L, Kahn R, Lau EHY, Chen D, Wong JY, Ali ST, Wu P, Cowling BJ. Joint Estimation of Generation Time and Incubation Period for Coronavirus Disease 2019. J Infect Dis 2021; 224:1664-1671. [PMID: 34423821 PMCID: PMC8499762 DOI: 10.1093/infdis/jiab424] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 08/21/2021] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) has caused a heavy disease burden globally. The impact of process and timing of data collection on the accuracy of estimation of key epidemiological distributions are unclear. Because infection times are typically unobserved, there are relatively few estimates of generation time distribution. METHODS We developed a statistical framework to jointly estimate generation time and incubation period from human-to-human transmission pairs, accounting for sampling biases. We applied the framework on 80 laboratory-confirmed human-to-human transmission pairs in China. We further inferred the infectiousness profile, serial interval distribution, proportions of presymptomatic transmission, and basic reproduction number (R0) for COVID-19. RESULTS The estimated mean incubation period was 4.8 days (95% confidence interval [CI], 4.1-5.6), and mean generation time was 5.7 days (95% CI, 4.8-6.5). The estimated R0 based on the estimated generation time was 2.2 (95% CI, 1.9-2.4). A simulation study suggested that our approach could provide unbiased estimates, insensitive to the width of exposure windows. CONCLUSIONS Properly accounting for the timing and process of data collection is critical to have correct estimates of generation time and incubation period. R0 can be biased when it is derived based on serial interval as the proxy of generation time.
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Affiliation(s)
- Yiu Chung Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Tim K Tsang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Lee Kennedy-Shaffer
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Mathematics and Statistics, Vassar College, Poughkeepsie, New York, USA
| | - Rebecca Kahn
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Dongxuan Chen
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Jessica Y Wong
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
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185
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Empirical Analysis of the Impact of COVID-19 Social Distancing on Residential Electricity Consumption Based on Demographic Characteristics and Load Shape. ENERGIES 2021. [DOI: 10.3390/en14227523] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Since January 2020, the COVID-19 pandemic has been impacting various aspects of people’s daily lives and the economy. The first case of COVID-19 in South Korea was identified on 20 January 2020. The Korean government implemented the first social distancing measures in the first week of March 2020. As a result, energy consumption in the industrial, commercial and educational sectors decreased. On the other hand, residential energy consumption increased as telecommuting work and remote online classes were encouraged. However, the impact of social distancing on residential energy consumption in Korea has not been systematically analyzed. This study attempts to analyze the impact of social distancing implemented as a result of COVID-19 on residential energy consumption with time-varying reproduction numbers of COVID-19. A two-way fixed effect model and demographic characteristics are used to account for the heterogeneity. The changes in household energy consumption by load shape group are also analyzed with the household energy consumption model. There some are key results of COVID-19 impact on household energy consumption. Based on the hourly smart meter data, an average increase of 0.3% in the hourly average energy consumption is caused by a unit increase in the time-varying reproduction number of COVID-19. For each income, mid-income groups show less impact on energy consumption compared to both low-income and high-income groups. In each family member, as the number of family members increases, the change in electricity consumption affected by social distancing tends to decrease. For area groups, large area consumers increase household energy consumption more than other area groups. Lastly, The COVID-19 impact on each load shape is influenced by their energy consumption patterns.
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186
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Ram SK, Sornette D. Impact of Governmental interventions on epidemic progression and workplace activity during the COVID-19 outbreak. Sci Rep 2021; 11:21939. [PMID: 34753988 PMCID: PMC8578600 DOI: 10.1038/s41598-021-01276-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 09/09/2021] [Indexed: 01/09/2023] Open
Abstract
In the first quarter of 2020, the COVID-19 pandemic brought the world to a state of paralysis. During this period, humanity saw by far the largest organized travel restrictions and unprecedented efforts and global coordination to contain the spread of the SARS-CoV-2 virus. Using large scale human mobility and fine grained epidemic incidence data, we develop a framework to understand and quantify the effectiveness of the interventions implemented by various countries to control epidemic growth. Our analysis reveals the importance of timing and implementation of strategic policy in controlling the epidemic. We also unearth significant spatial diffusion of the epidemic before and during the lockdown measures in several countries, casting doubt on the effectiveness or on the implementation quality of the proposed Governmental policies.
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Affiliation(s)
- Sumit Kumar Ram
- Department of Management Technology and Economics, ETH Zurich, Zurich, Switzerland.
- Connection Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Didier Sornette
- Department of Management Technology and Economics, ETH Zurich, Zurich, Switzerland.
- Department of Earth Sciences, ETH Zurich, Zurich, Switzerland.
- Department of Physics, ETH Zurich, Zurich, Switzerland.
- Swiss Finance Institute c/o University of Geneva, Geneva, Switzerland.
- Institute of Risk Analysis, Prediction and Management (Risks-X), Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology (SUSTech), Shenzhen, 518055, China.
- Tokyo Tech World Research Hub Initiative, Institute of Innovative Research, Tokyo Institute of Technology, Tokyo, Japan.
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187
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Price BS, Khodaverdi M, Halasz A, Hendricks B, Kimble W, Smith GS, Hodder SL. Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach. PLoS One 2021; 16:e0259538. [PMID: 34731188 PMCID: PMC8565789 DOI: 10.1371/journal.pone.0259538] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 10/20/2021] [Indexed: 11/18/2022] Open
Abstract
During the COVID-19 pandemic, West Virginia developed an aggressive SARS-CoV-2 testing strategy which included utilizing pop-up mobile testing in locations anticipated to have near-term increases in SARS-CoV-2 infections. This study describes and compares two methods for predicting near-term SARS-CoV-2 incidence in West Virginia counties. The first method, Rt Only, is solely based on producing forecasts for each county using the daily instantaneous reproductive numbers, Rt. The second method, ML+Rt, is a machine learning approach that uses a Long Short-Term Memory network to predict the near-term number of cases for each county using epidemiological statistics such as Rt, county population information, and time series trends including information on major holidays, as well as leveraging statewide COVID-19 trends across counties and county population size. Both approaches used daily county-level SARS-CoV-2 incidence data provided by the West Virginia Department Health and Human Resources beginning April 2020. The methods are compared on the accuracy of near-term SARS-CoV-2 increases predictions by county over 17 weeks from January 1, 2021- April 30, 2021. Both methods performed well (correlation between forecasted number of cases and the actual number of cases week over week is 0.872 for the ML+Rt method and 0.867 for the Rt Only method) but differ in performance at various time points. Over the 17-week assessment period, the ML+Rt method outperforms the Rt Only method in identifying larger spikes. Results show that both methods perform adequately in both rural and non-rural predictions. Finally, a detailed discussion on practical issues regarding implementing forecasting models for public health action based on Rt is provided, and the potential for further development of machine learning methods that are enhanced by Rt.
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Affiliation(s)
- Bradley S. Price
- West Virginia Clinical and Translational Science Institute, Morgantown, West Virginia, United States of America
- Management Information Systems Department, West Virginia University, Morgantown, West Virginia, United States of America
| | - Maryam Khodaverdi
- West Virginia Clinical and Translational Science Institute, Morgantown, West Virginia, United States of America
| | - Adam Halasz
- School of Mathematics and Data Science, West Virginia University, Morgantown, West Virginia, United States of America
| | - Brian Hendricks
- Department of Epidemiology and Biostatistics, West Virginia University, Morgantown, West Virginia, United States of America
| | - Wesley Kimble
- West Virginia Clinical and Translational Science Institute, Morgantown, West Virginia, United States of America
| | - Gordon S. Smith
- Department of Epidemiology and Biostatistics, West Virginia University, Morgantown, West Virginia, United States of America
| | - Sally L. Hodder
- West Virginia Clinical and Translational Science Institute, Morgantown, West Virginia, United States of America
- West Virginia University School of Medicine, Morgantown, West Virginia, United States of America
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188
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Paltiel AD, Schwartz JL. Assessing COVID-19 Prevention Strategies to Permit the Safe Opening of Residential Colleges in Fall 2021. Ann Intern Med 2021; 174:1563-1571. [PMID: 34461034 PMCID: PMC8422997 DOI: 10.7326/m21-2965] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Effective vaccines, improved testing technologies, and decreases in COVID-19 incidence prompt an examination of the choices available to residential college administrators seeking to safely resume in-person campus activities in fall 2021. OBJECTIVE To help college administrators design and evaluate customized COVID-19 safety plans. DESIGN Decision analysis using a compartmental epidemic model to optimize vaccination, testing, and other nonpharmaceutical interventions depending on decision makers' preferences, choices, and assumptions about epidemic severity and vaccine effectiveness against infection, transmission, and disease progression. SETTING U.S. residential colleges. PARTICIPANTS Hypothetical cohort of 5000 persons (students, faculty, and staff) living and working in close proximity on campus. MEASUREMENTS Cumulative infections over a 120-day semester. RESULTS Under base-case assumptions, if 90% coverage can be attained with a vaccine that is 85% protective against infection and 25% protective against asymptomatic transmission, the model finds that campus activities can be resumed while holding cumulative cases below 5% of the population without the need for routine, asymptomatic testing. With 50% population coverage using such a vaccine, a similar cap on cumulative cases would require either daily asymptomatic testing of unvaccinated persons or a combination of less frequent testing and resumption of aggressive distancing and other nonpharmaceutical prevention policies. Colleges returning to pre-COVID-19 campus activities without either broad vaccination coverage or high-frequency testing put their campus population at risk for widespread viral transmission. LIMITATION Uncertainty in data, particularly vaccine effectiveness (preventive and transmission); no distinguishing between students and employees; and assumes limited community intermixing. CONCLUSION Vaccination coverage is the most powerful tool available to residential college administrators seeking to achieve a safe return to prepandemic operations this fall. Given the breadth of potential outcomes in the face of uncontrollable and uncertain factors, even colleges with high vaccination rates should be prepared to reinstitute or expand testing and distancing policies on short notice. PRIMARY FUNDING SOURCE National Institute on Drug Abuse.
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Affiliation(s)
- A David Paltiel
- Yale School of Public Health, New Haven, Connecticut (A.D.P., J.L.S.)
| | - Jason L Schwartz
- Yale School of Public Health, New Haven, Connecticut (A.D.P., J.L.S.)
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Abstract
Abstract
Evaluating the effectiveness of nonpharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic is crucial to maximize the epidemic containment while minimizing the social and economic impact of these measures. However, this endeavor crucially relies on surveillance data publicly released by health authorities that can hide several limitations. In this article, we quantify the impact of inaccurate data on the estimation of the time-varying reproduction number
$ R(t) $
, a pivotal quantity to gauge the variation of the transmissibility originated by the implementation of different NPIs. We focus on Italy and Spain, two European countries among the most severely hit by the COVID-19 pandemic. For these two countries, we highlight several biases of case-based surveillance data and temporal and spatial limitations in the data regarding the implementation of NPIs. We also demonstrate that a nonbiased estimation of
$ R(t) $
could have had direct consequences on the decisions taken by the Spanish and Italian governments during the first wave of the pandemic. Our study shows that extreme care should be taken when evaluating intervention policies through publicly available epidemiological data and call for an improvement in the process of COVID-19 data collection, management, storage, and release. Better data policies will allow a more precise evaluation of the effects of containment measures, empowering public health authorities to take more informed decisions.
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190
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Sachak-Patwa R, Byrne HM, Dyson L, Thompson RN. The risk of SARS-CoV-2 outbreaks in low prevalence settings following the removal of travel restrictions. COMMUNICATIONS MEDICINE 2021; 1:39. [PMID: 35602220 PMCID: PMC9053223 DOI: 10.1038/s43856-021-00038-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 09/03/2021] [Indexed: 12/23/2022] Open
Abstract
Background Countries around the world have introduced travel restrictions to reduce SARS-CoV-2 transmission. As vaccines are gradually rolled out, attention has turned to when travel restrictions and other non-pharmaceutical interventions (NPIs) can be relaxed. Methods Using SARS-CoV-2 as a case study, we develop a mathematical branching process model to assess the risk that, following the removal of NPIs, cases arriving in low prevalence settings initiate a local outbreak. Our model accounts for changes in background population immunity due to vaccination. We consider two locations with low prevalence in which the vaccine rollout has progressed quickly – specifically, the Isle of Man (a British crown dependency in the Irish Sea) and the country of Israel. Results We show that the outbreak risk is unlikely to be eliminated completely when travel restrictions and other NPIs are removed. This general result is the most important finding of this study, rather than exact quantitative outbreak risk estimates in different locations. It holds even once vaccine programmes are completed. Key factors underlying this result are the potential for transmission even following vaccination, incomplete vaccine uptake, and the recent emergence of SARS-CoV-2 variants with increased transmissibility. Conclusions Combined, the factors described above suggest that, when travel restrictions are relaxed, it may still be necessary to implement surveillance of incoming passengers to identify infected individuals quickly. This measure, as well as tracing and testing (and/or isolating) contacts of detected infected passengers, remains useful to suppress potential outbreaks while global case numbers are high. The effectiveness of public health measures against COVID-19 has varied between countries, with some experiencing many infections and others containing transmission successfully. As vaccines are deployed, an important challenge is deciding when to relax measures. Here, we consider locations with few cases, and investigate whether vaccination can ever eliminate the risk of COVID-19 outbreaks completely, allowing measures to be removed risk-free. Using a mathematical model, we demonstrate that there is still a risk that imported cases initiate outbreaks when measures are removed, even if most of the population is fully vaccinated. This highlights the need for continued vigilance in low prevalence settings to prevent imported cases leading to local transmission. Until case numbers are reduced globally, so that SARS-CoV-2 spread between countries is less likely, the risk of outbreaks in low prevalence settings will remain. Sachak-Patwa et al. estimate the risk of SARS-CoV-2 outbreaks in low prevalence settings following the removal of travel restrictions and other non-pharmaceutical interventions, with the Isle of Man and Israel as case studies. Using a branching process mathematical model, the authors show that even after a large proportion of the population is vaccinated, there remains a risk of local outbreaks from imported cases.
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Price BS, Khodaverdi M, Halasz A, Hendricks B, Kimble W, Smith GS, Hodder SL. Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.10.06.21264569. [PMID: 34642701 PMCID: PMC8509102 DOI: 10.1101/2021.10.06.21264569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
During the COVID-19 pandemic, West Virginia developed an aggressive SARS-CoV-2 testing strategy which included utilizing pop-up mobile testing in locations anticipated to have near-term increases in SARS-CXoV-2 infections. In this study, we describe and compare two methods for predicting near-term SARS-CoV-2 incidence in West Virginia counties. The first method, R t Only, is solely based on producing forecasts for each county using the daily instantaneous reproductive numbers, R t. The second method, ML+ R t , is a machine learning approach that uses a Long Short-Term Memory network to predict the near-term number of cases for each county using epidemiological statistics such as Rt, county population information, and time series trends including information on major holidays, as well as leveraging statewide COVID-19 trends across counties and county population size. Both approaches used daily county-level SARS-CoV-2 incidence data provided by the West Virginia Department Health and Human Resources beginning April 2020. The methods are compared on the accuracy of near-term SARS-CoV-2 increases predictions by county over 17 weeks from January 1, 2021-April 30, 2021. Both methods performed well (correlation between forecasted number of cases and the actual number of cases week over week is 0.872 for the ML+R t method and 0.867 for the R t Only method) but differ in performance at various time points. Over the 17-week assessment period, the ML+R t method outperforms the R t Only method in identifying larger spikes. We also find that both methods perform adequately in both rural and non-rural predictions. Finally, we provide a detailed discussion on practical issues regarding implementing forecasting models for public health action based on R t , and the potential for further development of machine learning methods that are enhanced by R t.
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Affiliation(s)
- Bradley S. Price
- Management Information Systems Department, West Virginia University, Morgantown, West Virginia
| | - Maryam Khodaverdi
- West Virginia Clinical and Translational Science Institute, Morgantown, West Virginia
| | - Adam Halasz
- School of Mathematics and Data Science, West Virginia University, Morgantown, West Virginia
| | - Brian Hendricks
- Department of Epidemiology and Biostatistics, West Virginia University, Morgantown, West Virginia
| | - Wesley Kimble
- West Virginia Clinical and Translational Science Institute, Morgantown, West Virginia
| | - Gordon S. Smith
- Department of Epidemiology and Biostatistics, West Virginia University, Morgantown, West Virginia
| | - Sally L. Hodder
- West Virginia Clinical and Translational Science Institute, Morgantown, West Virginia
- West Virginia University School of Medicine, Morgantown, West Virginia
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192
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Xu R, Rahmandad H, Gupta M, DiGennaro C, Ghaffarzadegan N, Amini H, Jalali MS. Weather, air pollution, and SARS-CoV-2 transmission: a global analysis. Lancet Planet Health 2021; 5:e671-e680. [PMID: 34627471 PMCID: PMC8497024 DOI: 10.1016/s2542-5196(21)00202-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 06/12/2021] [Accepted: 07/19/2021] [Indexed: 05/21/2023]
Abstract
BACKGROUND Understanding how environmental factors affect SARS-CoV-2 transmission could inform global containment efforts. Despite high scientific and public interest and multiple research reports, there is currently no consensus on the association of environmental factors and SARS-CoV-2 transmission. To address this research gap, we aimed to assess the relative risk of transmission associated with weather conditions and ambient air pollution. METHODS In this global analysis, we adjusted for the delay between infection and detection, estimated the daily reproduction number at 3739 global locations during the COVID-19 pandemic up until late April, 2020, and investigated its associations with daily local weather conditions (ie, temperature, humidity, precipitation, snowfall, moon illumination, sunlight hours, ultraviolet index, cloud cover, wind speed and direction, and pressure data) and ambient air pollution (ie, PM2·5, nitrogen dioxide, ozone, and sulphur dioxide). To account for other confounding factors, we included both location-specific fixed effects and trends, controlling for between-location differences and heterogeneities in locations' responses over time. We built confidence in our estimations through synthetic data, robustness, and sensitivity analyses, and provided year-round global projections for weather-related risk of global SARS-CoV-2 transmission. FINDINGS Our dataset included data collected between Dec 12, 2019, and April 22, 2020. Several weather variables and ambient air pollution were associated with the spread of SARS-CoV-2 across 3739 global locations. We found a moderate, negative relationship between the estimated reproduction number and temperatures warmer than 25°C (a decrease of 3·7% [95% CI 1·9-5·4] per additional degree), a U-shaped relationship with outdoor ultraviolet exposure, and weaker positive associations with air pressure, wind speed, precipitation, diurnal temperature, sulphur dioxide, and ozone. Results were robust to multiple assumptions. Independent research building on our estimates provides strong support for the resulting projections across nations. INTERPRETATION Warmer temperature and moderate outdoor ultraviolet exposure result in a slight reduction in the transmission of SARS-CoV-2; however, changes in weather or air pollution alone are not enough to contain the spread of SARS-CoV-2 with other factors having greater effects. FUNDING None.
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Affiliation(s)
- Ran Xu
- Department of Allied Health Sciences, University of Connecticut, Storrs, CT, USA
| | - Hazhir Rahmandad
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Marichi Gupta
- Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA, USA
| | - Catherine DiGennaro
- Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA, USA
| | - Navid Ghaffarzadegan
- Department of Industrial and Systems Engineering, Virginia Tech, Falls Church, VA, USA
| | - Heresh Amini
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark; Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Mohammad S Jalali
- Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA, USA; MGH Institute for Technology Assessment, Harvard Medical School, Harvard University, Boston, MA, USA.
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193
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Howerton E, Ferrari MJ, Bjørnstad ON, Bogich TL, Borchering RK, Jewell CP, Nichols JD, Probert WJM, Runge MC, Tildesley MJ, Viboud C, Shea K. Synergistic interventions to control COVID-19: Mass testing and isolation mitigates reliance on distancing. PLoS Comput Biol 2021; 17:e1009518. [PMID: 34710096 PMCID: PMC8553097 DOI: 10.1371/journal.pcbi.1009518] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 10/01/2021] [Indexed: 01/10/2023] Open
Abstract
Stay-at-home orders and shutdowns of non-essential businesses are powerful, but socially costly, tools to control the pandemic spread of SARS-CoV-2. Mass testing strategies, which rely on widely administered frequent and rapid diagnostics to identify and isolate infected individuals, could be a potentially less disruptive management strategy, particularly where vaccine access is limited. In this paper, we assess the extent to which mass testing and isolation strategies can reduce reliance on socially costly non-pharmaceutical interventions, such as distancing and shutdowns. We develop a multi-compartmental model of SARS-CoV-2 transmission incorporating both preventative non-pharmaceutical interventions (NPIs) and testing and isolation to evaluate their combined effect on public health outcomes. Our model is designed to be a policy-guiding tool that captures important realities of the testing system, including constraints on test administration and non-random testing allocation. We show how strategic changes in the characteristics of the testing system, including test administration, test delays, and test sensitivity, can reduce reliance on preventative NPIs without compromising public health outcomes in the future. The lowest NPI levels are possible only when many tests are administered and test delays are short, given limited immunity in the population. Reducing reliance on NPIs is highly dependent on the ability of a testing program to identify and isolate unreported, asymptomatic infections. Changes in NPIs, including the intensity of lockdowns and stay at home orders, should be coordinated with increases in testing to ensure epidemic control; otherwise small additional lifting of these NPIs can lead to dramatic increases in infections, hospitalizations and deaths. Importantly, our results can be used to guide ramp-up of testing capacity in outbreak settings, allow for the flexible design of combined interventions based on social context, and inform future cost-benefit analyses to identify efficient pandemic management strategies.
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Affiliation(s)
- Emily Howerton
- Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Department of Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Matthew J. Ferrari
- Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Department of Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Ottar N. Bjørnstad
- Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Department of Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Tiffany L. Bogich
- Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Department of Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Rebecca K. Borchering
- Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Department of Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Chris P. Jewell
- Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
| | - James D. Nichols
- U.S. Geological Survey, Eastern Ecological Science Center at the Patuxent Research Refuge, Laurel, Maryland, United States of America
| | - William J. M. Probert
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Michael C. Runge
- U.S. Geological Survey, Eastern Ecological Science Center at the Patuxent Research Refuge, Laurel, Maryland, United States of America
| | - Michael J. Tildesley
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), Mathematics Institute and School of Life Sciences, University of Warwick, Coventry, United Kingdom
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Katriona Shea
- Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Department of Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
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194
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Takahashi S, Peluso MJ, Hakim J, Turcios K, Janson O, Routledge I, Busch MP, Hoh R, Tai V, Kelly JD, Martin JN, Deeks SG, Henrich TJ, Greenhouse B, Rodríguez-Barraquer I. SARS-CoV-2 serology across scales: a framework for unbiased seroprevalence estimation incorporating antibody kinetics and epidemic recency. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.09.09.21263139. [PMID: 34545373 PMCID: PMC8452112 DOI: 10.1101/2021.09.09.21263139] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Serosurveys are a key resource for measuring SARS-CoV-2 cumulative incidence. A growing body of evidence suggests that asymptomatic and mild infections (together making up over 95% of all infections) are associated with lower antibody titers than severe infections. Antibody levels also peak a few weeks after infection and decay gradually. We developed a statistical approach to produce adjusted estimates of seroprevalence from raw serosurvey results that account for these sources of spectrum bias. We incorporate data on antibody responses on multiple assays from a post-infection longitudinal cohort, along with epidemic time series to account for the timing of a serosurvey relative to how recently individuals may have been infected. We applied this method to produce adjusted seroprevalence estimates from five large-scale SARS-CoV-2 serosurveys across different settings and study designs. We identify substantial differences between reported and adjusted estimates of over two-fold in the results of some surveys, and provide a tool for practitioners to generate adjusted estimates with pre-set or custom parameter values. While unprecedented efforts have been launched to generate SARS-CoV-2 seroprevalence estimates over this past year, interpretation of results from these studies requires properly accounting for both population-level epidemiologic context and individual-level immune dynamics.
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Affiliation(s)
- Saki Takahashi
- Division of HIV, Infectious Diseases & Global Medicine, University of California, San Francisco, USA
| | - Michael J. Peluso
- Division of HIV, Infectious Diseases & Global Medicine, University of California, San Francisco, USA
| | - Jill Hakim
- Division of HIV, Infectious Diseases & Global Medicine, University of California, San Francisco, USA
| | - Keirstinne Turcios
- Division of HIV, Infectious Diseases & Global Medicine, University of California, San Francisco, USA
| | - Owen Janson
- Division of HIV, Infectious Diseases & Global Medicine, University of California, San Francisco, USA
| | - Isobel Routledge
- Division of HIV, Infectious Diseases & Global Medicine, University of California, San Francisco, USA
| | - Michael P. Busch
- Department of Laboratory Medicine, University of California, San Francisco, USA
- Vitalant Research Institute, San Francisco, USA
| | - Rebecca Hoh
- Division of HIV, Infectious Diseases & Global Medicine, University of California, San Francisco, USA
| | - Viva Tai
- Division of HIV, Infectious Diseases & Global Medicine, University of California, San Francisco, USA
| | - J. Daniel Kelly
- Department of Epidemiology and Biostatistics, University of California, San Francisco, USA
- Institute for Global Health Sciences, University of California, San Francisco, USA
- F.I. Proctor Foundation, University of California, San Francisco, USA
| | - Jeffrey N. Martin
- Department of Epidemiology and Biostatistics, University of California, San Francisco, USA
| | - Steven G. Deeks
- Division of HIV, Infectious Diseases & Global Medicine, University of California, San Francisco, USA
| | - Timothy J. Henrich
- Division of Experimental Medicine, University of California, San Francisco, USA
| | - Bryan Greenhouse
- Division of HIV, Infectious Diseases & Global Medicine, University of California, San Francisco, USA
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195
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Munday JD, Jarvis CI, Gimma A, Wong KLM, van Zandvoort K, Funk S, Edmunds WJ. Estimating the impact of reopening schools on the reproduction number of SARS-CoV-2 in England, using weekly contact survey data. BMC Med 2021; 19:233. [PMID: 34503493 PMCID: PMC8428960 DOI: 10.1186/s12916-021-02107-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 08/26/2021] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Schools were closed in England on 4 January 2021 as part of increased national restrictions to curb transmission of SARS-CoV-2. The UK government reopened schools on 8 March. Although there was evidence of lower individual-level transmission risk amongst children compared to adults, the combined effects of this with increased contact rates in school settings and the resulting impact on the overall transmission rate in the population were not clear. METHODS We measured social contacts of > 5000 participants weekly from March 2020, including periods when schools were both open and closed, amongst other restrictions. We combined these data with estimates of the susceptibility and infectiousness of children compared with adults to estimate the impact of reopening schools on the reproduction number. RESULTS Our analysis indicates that reopening all schools under the same measures as previous periods that combined lockdown with face-to-face schooling would be likely to increase the reproduction number substantially. Assuming a baseline of 0.8, we estimated a likely increase to between 1.0 and 1.5 with the reopening of all schools or to between 0.9 and 1.2 reopening primary or secondary schools alone. CONCLUSION Our results suggest that reopening schools would likely halt the fall in cases observed between January and March 2021 and would risk a return to rising infections, but these estimates relied heavily on the latest estimates or reproduction number and the validity of the susceptibility and infectiousness profiles we used at the time of reopening.
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Affiliation(s)
- James D Munday
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK.
| | - Christopher I Jarvis
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK
| | - Amy Gimma
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK
| | - Kerry L M Wong
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK
| | - Kevin van Zandvoort
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK
| | - Sebastian Funk
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK
| | - W John Edmunds
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK
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196
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Crucinio FR, Doucet A, Johansen AM. A Particle Method for Solving Fredholm Equations of the First Kind. J Am Stat Assoc 2021. [DOI: 10.1080/01621459.2021.1962328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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197
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Parag KV. Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves. PLoS Comput Biol 2021; 17:e1009347. [PMID: 34492011 PMCID: PMC8448340 DOI: 10.1371/journal.pcbi.1009347] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 09/17/2021] [Accepted: 08/13/2021] [Indexed: 12/15/2022] Open
Abstract
We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number, R, from the incidence of an infectious disease in real time and retrospectively. Our approach borrows from Kalman filtering theory, is quick and easy to compute, generalisable, deterministic and unlike many current methods, requires no change-point or window size assumptions. We model R as a flexible, hidden Markov state process and exactly solve forward-backward algorithms, to derive R estimates that incorporate all available incidence information. This unifies and extends two popular methods, EpiEstim, which considers past incidence, and the Wallinga-Teunis method, which looks forward in time. We find that this combination of maximising information and minimising assumptions significantly reduces the bias and variance of R estimates. Moreover, these properties make EpiFilter more statistically robust in periods of low incidence, where several existing methods can become destabilised. As a result, EpiFilter offers improved inference of time-varying transmission patterns that are advantageous for assessing the risk of upcoming waves of infection or the influence of interventions, in real time and at various spatial scales.
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Affiliation(s)
- Kris V. Parag
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
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198
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Pitzer VE, Chitwood M, Havumaki J, Menzies NA, Perniciaro S, Warren JL, Weinberger DM, Cohen T. The Impact of Changes in Diagnostic Testing Practices on Estimates of COVID-19 Transmission in the United States. Am J Epidemiol 2021; 190:1908-1917. [PMID: 33831148 PMCID: PMC8083380 DOI: 10.1093/aje/kwab089] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 03/22/2021] [Accepted: 03/26/2021] [Indexed: 12/28/2022] Open
Abstract
Estimates of the reproductive number for novel pathogens such as severe acute respiratory syndrome coronavirus 2 are essential for understanding the potential trajectory of the epidemic and the level of intervention that is needed to bring the epidemic under control. However, most methods for estimating the basic reproductive number (R0) and time-varying effective reproductive number (Rt) assume that the fraction of cases detected and reported is constant through time. We explore the impact of secular changes in diagnostic testing and reporting on estimates of R0 and Rt using simulated data. We then compare these patterns to data on reported cases of coronavirus disease and testing practices from different states in the United States from March 4 to August 30, 2020. We find that changes in testing practices and delays in reporting can result in biased estimates of R0 and Rt. Examination of changes in the daily number of tests conducted and the percent of patients testing positive may be helpful for identifying the potential direction of bias. Changes in diagnostic testing and reporting processes should be monitored and taken into consideration when interpreting estimates of the reproductive number of coronavirus disease.
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Affiliation(s)
- Virginia E Pitzer
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, Yale University, New Haven, Connecticut, United States
- Correspondence to: Virginia E. Pitzer, Yale School of Public Health, P.O. Box 208034, New Haven, CT 06520-8034 (e-mail: )
| | - Melanie Chitwood
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, Yale University, New Haven, Connecticut, United States
| | - Joshua Havumaki
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, Yale University, New Haven, Connecticut, United States
| | - Nicolas A Menzies
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States
| | - Stephanie Perniciaro
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, Yale University, New Haven, Connecticut, United States
| | - Joshua L Warren
- Department of Biostatistics and Public Health Modeling Unit, Yale School of Public Health, Yale University, New Haven, Connecticut, United States
| | - Daniel M Weinberger
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, Yale University, New Haven, Connecticut, United States
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, Yale University, New Haven, Connecticut, United States
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199
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Parag KV. Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves. PLoS Comput Biol 2021. [PMID: 34492011 DOI: 10.1101/2020.09.14.20194589v1.abstract] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2023] Open
Abstract
We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number, R, from the incidence of an infectious disease in real time and retrospectively. Our approach borrows from Kalman filtering theory, is quick and easy to compute, generalisable, deterministic and unlike many current methods, requires no change-point or window size assumptions. We model R as a flexible, hidden Markov state process and exactly solve forward-backward algorithms, to derive R estimates that incorporate all available incidence information. This unifies and extends two popular methods, EpiEstim, which considers past incidence, and the Wallinga-Teunis method, which looks forward in time. We find that this combination of maximising information and minimising assumptions significantly reduces the bias and variance of R estimates. Moreover, these properties make EpiFilter more statistically robust in periods of low incidence, where several existing methods can become destabilised. As a result, EpiFilter offers improved inference of time-varying transmission patterns that are advantageous for assessing the risk of upcoming waves of infection or the influence of interventions, in real time and at various spatial scales.
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Affiliation(s)
- Kris V Parag
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
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Harvey A, Kattuman P. A farewell to R: time-series models for tracking and forecasting epidemics. J R Soc Interface 2021; 18:20210179. [PMID: 34583564 PMCID: PMC8479341 DOI: 10.1098/rsif.2021.0179] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 09/02/2021] [Indexed: 11/12/2022] Open
Abstract
The time-dependent reproduction number, Rt, is a key metric used by epidemiologists to assess the current state of an outbreak of an infectious disease. This quantity is usually estimated using time-series observations on new infections combined with assumptions about the distribution of the serial interval of transmissions. Bayesian methods are often used with the new cases data smoothed using a simple, but to some extent arbitrary, moving average. This paper describes a new class of time-series models, estimated by classical statistical methods, for tracking and forecasting the growth rate of new cases and deaths. Very few assumptions are needed and those that are made can be tested. Estimates of Rt, together with their standard deviations, are obtained as a by-product.
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Affiliation(s)
- Andrew Harvey
- Faculty of Economics, University of Cambridge, Cambridge, UK
| | - Paul Kattuman
- Cambridge Judge Business School, University of Cambridge, Cambridge, UK
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