Estimation and optimal control of the multiscale dynamics of Covid-19: a case study from Cameroon

This work aims at a better understanding and the optimal control of the spread of the new severe acute respiratory corona virus 2 (SARS-CoV-2). We first propose a multi-scale model giving insights on the virus population dynamics, the transmission process and the infection mechanism. We consider 10 compartments in the human population in order to take into accounts the effects of different specific mitigation policies. The population of viruses is also partitioned into 10 compartments corresponding respectively to each of the first nine human population compartments and the free viruses available in the environment. We show the global stability of the disease free equilibrium if a given threshold T0 is less or equal to 1 and we provide how to compute the basic reproduction number R0. A convergence index T1 is also defined in order to estimate the speed at which the disease extincts and an upper bound to the time of extinction is given. The existence of the endemic equilibrium is conditional and its description is provided. We evaluate the sensitivity of R0, T0 and T1 to control parameters such as the maximal human density allowed per unit of surface, the rate of disinfection both for people and environment, the mobility probability, the wearing mask probability or efficiency, and the human to human contact rate which results from the previous one. According to a functional cost taking into consideration economic impacts of SARS-CoV-2, we determine and discuss optimal fighting strategies. The study is applied to available data from Cameroon.

[1]  D. Kirschner,et al.  A methodology for performing global uncertainty and sensitivity analysis in systems biology. , 2008, Journal of theoretical biology.

[2]  V. Chongsuvivatwong,et al.  Optimal control on a mathematical model to pattern the progression of coronavirus disease 2019 (COVID-19) in Indonesia , 2020, Global Health Research and Policy.

[3]  James Watmough,et al.  Further Notes on the Basic Reproduction Number , 2008 .

[4]  P. Colaneri,et al.  Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy , 2020, Nature Medicine.

[5]  C. Hameni Nkwayep,et al.  Short-term forecasts of the COVID-19 pandemic: a study case of Cameroon , 2020, Chaos, Solitons & Fractals.

[6]  Winston Garira,et al.  A complete categorization of multiscale models of infectious disease systems , 2017, Journal of biological dynamics.

[7]  Pierre Magal,et al.  The parameter identification problem for SIR epidemic models: identifying unreported cases , 2018, Journal of mathematical biology.

[8]  Jürgen Kurths,et al.  Modeling and parameter Estimation of tuberculosis with Application to Cameroon , 2011, Int. J. Bifurc. Chaos.

[9]  M. Kraemer,et al.  Preparedness and vulnerability of African countries against importations of COVID-19: a modelling study , 2020, The Lancet.

[10]  Y. Terefe,et al.  Analysis of the mitigation strategies for COVID-19: From mathematical modelling perspective , 2020, Chaos, Solitons & Fractals.

[11]  P. van den Driessche,et al.  Reproduction numbers of infectious disease models , 2017, Infectious Disease Modelling.

[12]  F. Brauer,et al.  Mathematical Models in Population Biology and Epidemiology , 2001 .

[13]  M. R. Leadbetter Poisson Processes , 2011, International Encyclopedia of Statistical Science.

[14]  P. Alam ‘A’ , 2021, Composites Engineering: An A–Z Guide.

[15]  Chandini Raina MacIntyre,et al.  Mathematical assessment of the impact of non-pharmaceutical interventions on curtailing the 2019 novel Coronavirus , 2020, Mathematical Biosciences.

[16]  CIRD-F: Spread and Influence of COVID-19 in China , 2020, Journal of Shanghai Jiaotong University.

[17]  G. Webb,et al.  Understanding Unreported Cases in the COVID-19 Epidemic Outbreak in Wuhan, China, and the Importance of Major Public Health Interventions , 2020, Biology.

[18]  Sophie Pfeifer Experimental Design With Applications In Management Engineering And The Sciences , 2016 .

[19]  Amjad D. Al-Nasser,et al.  SARS-CoV-2 and Coronavirus Disease 2019: What We Know So Far , 2020, Pathogens.

[20]  J. Kurths,et al.  Parameter and state estimation in a Neisseria meningitidis model: A study case of Niger. , 2016, Chaos.

[21]  Jianhong Wu,et al.  Scenario Tree and Adaptive Decision Making on Optimal Type and Timing for Intervention and Social-economic Activity Changes to Manage the COVID-19 Pandemic , 2020 .

[22]  J. Hyman,et al.  Determining Important Parameters in the Spread of Malaria Through the Sensitivity Analysis of a Mathematical Model , 2008, Bulletin of mathematical biology.

[23]  Lin Wang,et al.  Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries , 2020, Chaos, Solitons & Fractals.

[24]  W. Garira,et al.  A mathematical modelling framework for linked within-host and between-host dynamics for infections with free-living pathogens in the environment. , 2014, Mathematical biosciences.

[25]  Sebastian Aniţa,et al.  An Introduction to Optimal Control Problems in Life Sciences and Economics: From Mathematical Models to Numerical Simulation with MATLAB® , 2010 .

[27]  Stelios Bekiros,et al.  SBDiEM: A new mathematical model of infectious disease dynamics , 2020, Chaos, Solitons & Fractals.

[28]  John T. Workman,et al.  Optimal Control Applied to Biological Models , 2007 .

[29]  Winston Garira,et al.  The research and development process for multiscale models of infectious disease systems , 2020, PLoS Comput. Biol..

[30]  Mostafa Rachik,et al.  A mathematical modeling with optimal control strategy of transmission of COVID-19 pandemic virus , 2020 .

[31]  Jérôme Harmand,et al.  The Chemostat: Mathematical Theory of Microorganism Cultures , 2017 .

[32]  Winston Garira,et al.  FROM INDIVIDUAL HEALTH TO COMMUNITY HEALTH: TOWARDS MULTISCALE MODELING OF DIRECTLY TRANSMITTED INFECTIOUS DISEASE SYSTEMS , 2019, Journal of Biological Systems.

[33]  G. Rempała,et al.  Multi-scale dynamics of infectious diseases , 2019, Interface Focus.

[34]  Tridip Sardar,et al.  Assessment of lockdown effect in some states and overall India: A predictive mathematical study on COVID-19 outbreak , 2020, Chaos, Solitons & Fractals.

[35]  Mobility restrictions for the control of epidemics: When do they work? , 2020, PloS one.

[36]  D. Zeng,et al.  Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies , 2020, medRxiv.

[37]  Zhen Jin,et al.  Analysis of COVID-19 transmission in Shanxi Province with discrete time imported cases. , 2020, Mathematical biosciences and engineering : MBE.

[38]  Erica L. Thompson,et al.  Asymptotic estimates of SARS-CoV-2 infection counts and their sensitivity to stochastic perturbation , 2020, Chaos.

[39]  M. Batista Estimation of the final size of the second phase of the coronavirus epidemic by the logistic model , 2020, medRxiv.

[40]  Tom Britton,et al.  Stochastic Epidemic Models with Inference , 2019, Lecture Notes in Mathematics.

[41]  E. Venturino,et al.  Analysis of a Model for Coronavirus Spread , 2020, Mathematics.

[42]  M. T. Sofonea,et al.  Age-structured non-pharmaceutical interventions for optimal control of COVID-19 epidemic , 2020, medRxiv.

[43]  M. Marchesin,et al.  ASSESSING THE EFFICIENCY OF DIFFERENT CONTROL STRATEGIES FOR THE COVID-19 EPIDEMIC , 2020 .

[44]  Horst R. Thieme,et al.  Convergence results and a Poincaré-Bendixson trichotomy for asymptotically autonomous differential equations , 1992 .

[45]  M. Keeling,et al.  Estimation of country-level basic reproductive ratios for novel Coronavirus (SARS-CoV-2/COVID-19) using synthetic contact matrices , 2020, PLoS Comput. Biol..

[46]  Hal L. Smith,et al.  Asymptotically autonomous semiflows: chain recurrence and Lyapunov functions , 1995 .

[47]  F. Piazza,et al.  Analysis and forecast of COVID-19 spreading in China, Italy and France , 2020, Chaos, Solitons & Fractals.

[48]  C. Castillo-Chavez,et al.  Mobility restrictions for the control of epidemics: When do they work? , 2019, PloS one.

[49]  M. Martcheva,et al.  Coupling Within-Host and Between-Host Infectious Diseases Models , 2015 .

[50]  L. Poon,et al.  Stability of SARS-CoV-2 in different environmental conditions , 2020, The Lancet Microbe.

[51]  Franck Jedrzejewski Introduction aux méthodes numériques , 2001 .

[52]  Gauthier Sallet,et al.  Computation of threshold conditions for epidemiological models and global stability of the disease-free equilibrium (DFE). , 2008, Mathematical biosciences.

[53]  Early stage COVID-19 disease dynamics in Germany: models and parameter identification , 2020, Journal of mathematics in industry.

[54]  M. Graffar [Modern epidemiology]. , 1971, Bruxelles medical.

[55]  J. Watmough,et al.  Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. , 2002, Mathematical biosciences.

[56]  Robert G Easterling,et al.  Fundamentals of Statistical Experimental Design and Analysis , 2015 .

[57]  R. Sarpong,et al.  Bio-inspired synthesis of xishacorenes A, B, and C, and a new congener from fuscol† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c9sc02572c , 2019, Chemical science.

[58]  A. Gumel,et al.  Could masks curtail the post-lockdown resurgence of COVID-19 in the US? , 2020, Mathematical Biosciences.

[59]  Michael Y. Li,et al.  Why is it difficult to accurately predict the COVID-19 epidemic? , 2020, Infectious Disease Modelling.

[60]  Mark Jit,et al.  Projecting social contact matrices in 152 countries using contact surveys and demographic data , 2017, PLoS Comput. Biol..

[61]  Geoffrey I. Webb,et al.  A COVID-19 epidemic model with latency period , 2020, Infectious Disease Modelling.

[62]  Shigui Ruan,et al.  Structured population models in biology and epidemiology , 2008 .

[63]  Davide La Torre,et al.  Optimal control of prevention and treatment in a basic macroeconomic-epidemiological model , 2019, Math. Soc. Sci..

[64]  Michel Lejeune Statistique : la théorie et ses applications , 2010 .

[65]  E. Kostelich,et al.  To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic , 2020, Infectious Disease Modelling.

[66]  M. T. Sofonea,et al.  Optimal COVID-19 epidemic control until vaccine deployment , 2020, medRxiv.

[67]  P. Alam ‘E’ , 2021, Composites Engineering: An A–Z Guide.

[68]  Jean-Christophe Mourrat Processus stochastiques , 2014 .

[69]  Swapan Kumar Nandi,et al.  A model based study on the dynamics of COVID-19: Prediction and control , 2020, Chaos, Solitons & Fractals.