key: cord-0849996-od97az43 authors: Rahman, Azizur; Kuddus, Md Abdul title: Modelling the transmission dynamics of COVID-19 in six high burden countries date: 2020-04-25 journal: nan DOI: 10.1101/2020.04.22.20075192 sha: 9216aa9c871d17f95555ed83642f4d2130f5aa77 doc_id: 849996 cord_uid: od97az43 The new coronavirus disease, officially known as COVID-19, originated in China in 2019 and has since spread around the globe. We presented a modified Susceptible-Latent-Infected-Removed (SLIR) compartmental model of COVID-19 disease transmission with nonlinear incidence during the epidemic period. We provided the model calibration to estimate parameters with day wise corona virus (COVID-19) data i.e. reported cases by worldometer from the period of 15th February to 30th March, 2020 in six high burden countries including Australia, Italy, Spain, USA, UK and Canada. We estimate transmission rates for each countries and found that the highest transmission rate country in Spain, which may be increase the new cases and deaths in Spain than the other countries. Sensitivity analysis was used to identify the most important parameters through the partial rank correlation coefficient method. We found that the transmission rate of COVID-19 had the largest influence on the prevalence. We also provides the prediction of new cases in COVID-19 until May 18, 2020 using the developed model and recommends, control strategies of COVID-19. The information that we generated from this study would be useful to the decision makers of various organizations across the world including the Ministry of Health in Australia, Italy, Spain, USA, UK and Canada to control COVID-19. as 30, 11591, 7716, 3143, 1408 and 89 with mortality ratios nearly 0.67%, 11.39%, 8.77%, 76 1,9%, 6.4% and 1.2% respectively [1] . Figure 1 shows the cumulative number of confirmed The highest burden of COVID-19 is not only dependent on the health system but also depend 83 quickly response. For example, in Italy, the first confirmed COVID-19 cases on February 15 84 and then after few days thousands of people infected by COVID-19. The problem is not that 85 the Italy government didn't respond to the COVID-19. The problem is that it always responded 86 slightly too slow and with slightly too much moderation. What has resulted in China reveals 87 that quarantine, social distance, and isolation of infected populations can contain the epidemic. 88 This impact of the COVID-19 response in China is advocating for many countries where 89 COVID-19 is starting to spread. However, it is unclear whether other countries can implement 90 the stringent measures China eventually adopted. Singapore and Hong Kong, both of which 91 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 25, 2020. In mathematical models, the incidence rate plays an important role in the transmission of 106 infectious diseases. The number of individuals who become infected per unit time is called the 107 incidence rate in the epidemiology perspective [11] . Here, we consider the nonlinear incidence The rest of the paper is structured as follows: Section 2 presents model descriptions. Sections 122 We considered a modified SLIR compartmental model of COVID-19 transmission with 123 nonlinear incidence between the following mutually exclusive compartments: S(t)susceptible 124 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 25, 2020. 152 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 25, 2020. Given non-negative initial conditions for the system above, it is straightforward to show that 154 each of the state variables remain non-negative for all t > 0. Moreover, summing equations 155 (1)-(4) we find that the size of the total population, N(t) satisfies 163 In this section we estimated the model parameters based on the available six countries COVID-164 19 reported cases data from the worldometers.info [1] . Figure . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 25, 2020. Sensitivity analysis is performed to investigate the parameters that process the greatest 197 influence on the model outputs [20, 21] . In this study, we performed the partial rank correlation In this paper, we presented a modified SLIR compartmental model with nonlinear incidence. 214 We estimate number of cases from COVID-19 infection and apply it to data from the COVID- Within the six different countries we found that Spain has the highest transmission rate than 221 the other selected countries, which may be increase massive number of COVID-19 cases and 222 make worst situation in Spain. We assume that initially Spain government may be not taken is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 25, 2020. . https://doi.org /10.1101 /10. /2020 There are so many ways that we can control COVID-19 transmission (i) wash your hands 233 regularly with soap and water or rubbing an alcohol-based sanitizer into your hands because 234 washing your hands kills viruses that may be on your hands, (ii) avoid touching your face as 235 much as possible because virus containing droplets on your hands can be transferred to your 236 eyes, mouth or nose where they can infect you, (iii) maintain at least 1.5 meters distance 237 between yourself and anyone who is coughing or sneezing because if you are too close to 238 someone you might breathe in droplets they cough or sneeze, (iv) make sure you and people 239 around you follow good respiratory hygiene. Respiratory hygiene is important because droplets 240 spread virus. By following good respiratory hygiene you catch any droplets that might be In this country, health system is very poor which leads to the fewer number of reported cases. Therefore, data from other countries, in particular the number of cases by date of COVID-19 249 onset is necessary to better understand the variability in cases across settings. are to reduce contact rates as well as increase the treatment rate that will be most effective way CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 25, 2020. This study based on aggregated COVID-19 surveillance data in Australia, Italy, Spain, USA, 267 UK and Canada taken from the worldometer. No confidential information included because 268 analyses were performed at the aggregate level. Therefore, no ethical approval is required. Coronvirus cases and deaths The role of mathematical modelling in guiding the science and 293 economics of malaria elimination Early dynamics of transmission and control of COVID-19: a mathematical modelling study COVID-19 and Italy: what next? Defining the in vivo phenotype of 301 artemisinin-resistant falciparum malaria: a modelling approach The last man standing is the most resistant: eliminating artemisinin-305 resistant malaria in Cambodia Modular programming for tuberculosis 307 control, the "AuTuMN" platform A user-friendly 309 mathematical modelling web interface to assist local decision making in the fight against drug-310 resistant tuberculosis 312 The risk of global epidemic replacement with drug-resistant Mycobacterium tuberculosis strains Mathematical modeling of infectious disease dynamics Incidence rates in dynamic populations Stability and bifurcation of an SIR epidemic model with nonlinear 319 incidence and treatment Global stability of two models with incomplete treatment for tuberculosis Population of Canada Cost-effective modeling of the transmission dynamics of malaria: A case 329 study in Bangladesh Mathematical model and intervention strategies for mitigating 332 tuberculosis in the Philippines Modelling optimal control of cholera in communities linked by 334 migration, Computational and mathematical methods in medicine Total Coronavirus Cases in Bangladesh The authors would like to thank the editorial office for their quick response and valuable 284 comments to produce the updated version.