key: cord-0822715-wcd2yy4e
authors: Aviv-Sharon, Elinor; Aharoni, Asaph
title: Generalized logistic growth modeling of the COVID-19 pandemic in Asia
date: 2020-07-24
journal: Infect Dis Model
DOI: 10.1016/j.idm.2020.07.003
sha: 49ff33b986e1201eb1d366d161646daf344e29fb
doc_id: 822715
cord_uid: wcd2yy4e

Several months into the ongoing novel coronavirus disease 2019 (COVID-19) pandemic, this work provides a simple and direct projection of the outbreak spreading potential and the pandemic cessation dates in Chinese mainland, Iran, the Philippines and Chinese Taiwan, using the generalized logistic model (GLM). The short-term predicted number of cumulative COVID-19 cases matched the confirmed reports of those who were infected across the four countries and regions, and the long-term forecasts were capable to accurately evaluate the spread of the pandemic in Chinese mainland and Chinese Taiwan, where control measures such as social distancing were fully implemented and sustained, suggesting GLM as a valuable tool for characterizing the transmission dynamics process and the trajectory of COVID-19 pandemic along with the impact of interventions.

Coronavirus disease 2019 , caused by the novel severe acute respiratory 29 syndrome coronavirus 2 (SARS-CoV-2) was identified in Chinese mainland, in 30 November 2019, declared to be a public health emergency of international concern in 31 two months, and recognized as a pandemic on 11 th March 2020. As of 1 st July 2020, 32 approximately 10.8 million cases of COVID-19 have been reported in 213 countries 33 and territories, resulting in approximately 520,000 deaths. Emerging infectious 34 diseases (EID), which appear in a population for the first time, or that may have 35 existed previously but is rapidly spreading, are possibly the deadliest and continue to 36 challenge human health. 37 As the world races to find a vaccine or a treatment to combat the pandemic, many 38 concerns arise about the outbreak severity, particularly the potential number of 39 infected people. Hence, it is of a great importance to estimate the outbreak evolution 

Specific countries/regions were selected as denoting different COVID-19 incidence 51 scales. Chinese mainland and Iran, the two major centers of COVID-19 outbreak in 52 eastern and southern Asia, respectively with tens of thousands of cases; the 53 Philippines, a representative of an archipelagic country with thousands of cases, and 54 Chinese Taiwan, with only hundreds of cases. For each country/region, the officially 55 reported data on COVID-19 daily cases from the onset of the outbreak to July 1 st , 56 2020 were collected from governmental or health authorities' websites (Appendix 57   Table, Figure1 A-D) . To allow outbreak projection, the data from the early phase of 58 the outbreak (35-40 days) were fitted with the GLM , an extension of the standard 59 logistic or sigmoid functions, as described previously (1, 5) . The originally logistic 60 model was developed by Verhulst in 1838 for biological populations growth 61 modelling (6) . Half a century ago, an extension of this classic logistic model, allowing 62 for more flexible curvature of the S shape where the growth curve is asymmetrical, 63 was introduced, establishing the Richards curve or generalized logistic model (7) . 64 This approach was chosen for its simplicity, minimal number of parameters, and for 65 its ability to capture the true extent of the prevalence of the pandemic. Inspired from 66 population biology, this model assumes an initial exponential growth phase that Where K is the upper asymptote, or the maximum cumulative case incidence and r is 72 the intrinsic growth rate during the exponential phase. t m is the turning point, the time 73 where maximum number of cases per day occur, is estimated as

The exponent measures the deviation from the symmetric classic logistic curve.

=1 illustrates a symmetric distribution centered at t m ; >1 or <1 indicates that Y(t) 76 grow faster or slower, respectively, than predicted by the model. Not a single new respectively, similar to those published previously (9) and to that of SARS (10). (Table) . Iran also has seen a rapid surge in the numbers of COVID-19 cases in recent weeks. 

Severe Acute Respiratory Syndrome Epidemic in Asia

Using Phenomenological Models to 228

Characterize Transmissibility and Forecast Patterns and Final Burden of Zika 229

Perspectives on 231 model forecasts of the 2014-2015 Ebola epidemic in West Africa: Lessons and

A Simple Procedure for Real-time Prediction of 234

Notice sur la loi que la population pursuit dans son accroissement

A flexible growth function for empirical use

Temporal dynamics in 240 viral shedding and transmissibility of COVID-19

Preliminary estimation of the basic 242 reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 243 to 2020: A data-driven analysis in the early phase of the outbreak

The generalized logistic growth model-predicted size of the COVID-19 pandemic in Chinese mainland, Iran, the Philippines and Chinese Taiwan

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.☒The authors declare no financial interests/personal relationships which may be considered as potential competing interests.