An Adaptive, Interacting, Cluster-Based Model Accurately Predicts the Transmission Dynamics of COVID-19

The SARS-CoV-2 driven disease, COVID-19, is presently a pandemic with increasing human and monetary costs. COVID-19 has put an unexpected and inordinate degree of pressure on healthcare systems of strong and fragile countries alike. In order to launch both containment and mitigation measures, each country requires accurate estimates of COVID-19 incidence as such preparedness allows agencies to plan efficient resource allocation and design control strategies. Here, we have developed a new adaptive, interacting, and cluster-based mathematical model to predict the granular trajectory COVID-19. We have analyzed incidence data from three currently afflicted countries of Italy, the United States of America, and India, and show that our approach predicts state-wise COVID-19 spread for each country with high accuracy. We show that R0 as the basic reproduction number exhibits significant spatial and temporal variation in these countries. However, by including a new function for temporal variation of R0 in an adaptive fashion, the predictive model provides highly reliable estimates of asymptomatic and undetected COVID-19 patients, both of which are key players in COVID-19 transmission. Our dynamic modeling approach can be applied widely and will provide a new fillip to infectious disease management strategies worldwide.

[1]  T. Lancet,et al.  The gendered dimensions of COVID-19 , 2020, The Lancet.

[2]  G. Gao,et al.  A Novel Coronavirus from Patients with Pneumonia in China, 2019 , 2020, The New England journal of medicine.

[3]  Zunyou Wu,et al.  Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. , 2020, JAMA.

[4]  Pejman Rohani,et al.  Appropriate Models for the Management of Infectious Diseases , 2005, PLoS medicine.

[5]  Kai Zhao,et al.  A pneumonia outbreak associated with a new coronavirus of probable bat origin , 2020, Nature.

[6]  M. Lipsitch,et al.  Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period , 2020, Science.

[7]  D. Holdstock.  Past, present--and future? , 2005, Medicine, conflict, and survival.

[8]  J. Vallino,et al.  A Review of Recent Developments in , 1997 .

[9]  Timothy F. Leslie,et al.  Complexity of the Basic Reproduction Number (R0) , 2019, Emerging infectious diseases.

[10]  P. Horby,et al.  COVID-19 and risks to the supply and quality of tests, drugs, and vaccines , 2020, The Lancet Global Health.

[11]  John S. Brownstein,et al.  Epidemiological data from the COVID-19 outbreak, real-time case information , 2020, Scientific Data.

[12]  Xihong Lin,et al.  Association of Public Health Interventions With the Epidemiology of the COVID-19 Outbreak in Wuhan, China. , 2020, JAMA.

[13]  E. Holmes,et al.  A new coronavirus associated with human respiratory disease in China , 2020, Nature.

[14]  Sirish L. Shah,et al.  Nonlinear Bayesian state estimation: A review of recent developments , 2012 .

[15]  N. Arinaminpathy,et al.  Prudent public health intervention strategies to control the coronavirus disease 2019 transmission in India: A mathematical model-based approach , 2020, The Indian journal of medical research.

[16]  Steve Leach,et al.  Transmission potential of smallpox in contemporary populations , 2001, Nature.

[17]  James O Lloyd-Smith,et al.  Dynamically Modeling SARS and Other Newly Emerging Respiratory Illnesses: Past, Present, and Future , 2005, Epidemiology.

[18]  M. Keeling,et al.  Modeling Infectious Diseases in Humans and Animals , 2007 .

[19]  Hannah R. Meredith,et al.  The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application , 2020, Annals of Internal Medicine.

[20]  Qun Li.  Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia , 2020 .

[21]  C. Fraser,et al.  Transmission Dynamics of the Etiological Agent of SARS in Hong Kong: Impact of Public Health Interventions , 2003, Science.

[22]  Jessica T Davis,et al.  The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak , 2020, Science.

[23]  F. Dominici,et al.  Aggregated mobility data could help fight COVID-19 , 2020, Science.

[24]  N. Petrosillo,et al.  COVID-19 R0: Magic number or conundrum? , 2020, Infectious disease reports.

[25]  Xiaolong Qi,et al.  Real estimates of mortality following COVID-19 infection , 2020, The Lancet Infectious Diseases.

[26]  T. Colbourn,et al.  COVID-19: extending or relaxing distancing control measures , 2020, The Lancet Public Health.

[27]  Lawrence J Lau,et al.  The COVID-19 Epidemic in China , 2020 .

[28]  P. Klepac,et al.  Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts , 2020, The Lancet Global Health.

[29]  J. Rocklöv,et al.  The reproductive number of COVID-19 is higher compared to SARS coronavirus , 2020, Journal of travel medicine.

[30]  Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China , 2020, Nature Medicine.

[31]  Carl A. B. Pearson,et al.  The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study , 2020, The Lancet Public Health.

[32]  Nuno R. Faria,et al.  The effect of human mobility and control measures on the COVID-19 epidemic in China , 2020, Science.

[33]  S. Strathdee,et al.  The coronavirus 2019-nCoV epidemic: Is hindsight 20/20? , 2020, EClinicalMedicine.

[34]  Jing Zhao,et al.  Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia , 2020, The New England journal of medicine.

[35]  P. Klepac,et al.  Early dynamics of transmission and control of COVID-19: a mathematical modelling study , 2020, The Lancet Infectious Diseases.