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org/10.1016/j.chaos.2020.110072
100. Yadav RP, Verma R (2020) A numerical simulation of fractional order mathematical
modeling of COVID-19 disease in case of Wuhan China. Chaos, Solitons Fractals
140:110124. https://doi.org/10.1016/j.chaos.2020.110124
101. KY Ng, MM Gui (2020) COVID-19: development of a robust mathematical model and
simulation package with consideration for ageing population and time delay for control
action and resusceptibility. Physica D Nonlinear Phenomena 411:132599. https://doi.org/
10.1016/j.physd.2020.132599Get
102. Ivorra B, Ferrández MR, Vela-Pérez M, Ramos AM (2020) Mathematical modeling of the
spread of the coronavirus disease 2019 (COVID-19) taking into account the undetected
infections. The case of China. Commun Nonlinear Sci Numer Simul 88:105303. https://doi.
org/10.1016/j.cnsns.2020.105303
103. HuyTuan N, Mohammadi H, Rezapour S (2020) A mathematical model for COVID-19
transmission by using the Caputo fractional derivative. Chaos Solitons Fractals 110107.
https://doi.org/10.1016/j.chaos.2020.110107
104. Sinkala M, Nkhoma P, Zulu M, Kafita D, Tembo R, Daka V. The COVID-19 pandemic in
Africa: predictions using the SIR Model. medRxiv 20118893. https://doi.org/10.1101/2020.
06.01.2011889
105. Agbokou1 K, Gneyou1 K, Tcharie K (2020) Investigation on the temporal evolution of the
covid’19pandemic: prediction for Togo. Open J Math Sci 4:273–279. https://doi.org/10.
30538/oms2020.0118. https://pisrt.org/psr-press/journals/oms
106. Pintér Gergő, Felde Imre, Mosavi Amir, Gloaguen Richard (2020) COVID-19 pandemic
prediction for Hungary; a hybrid machine learning approach. Mathematics 8:890. https://
doi.org/10.3390/math8060890www.mdpi.com/journal/mathematics
107. Cody Carroll et al (2020) Time dynamics of COVID-19. medRxiv 20109405. https://doi.
org/10.1101/2020.05.21.2010940
108. Humphrey L et al (2020) Testing, tracing and social distancing: assessing options for the
control of COVID_19. medRxiv 20077503. https://doi.org/10.1101/2020.04.23.20077503
109. Hoque E et al (2020) Adjusted dynamics of COVID-19 pandemic due to herd immunity in
Bangladesh. medRxiv 20186957. https://doi.org/10.1101/2020.09.03.20186957
110. Furati KM, Sarumi IO, Khaliq AQM (2020) Memory-dependent model for the dynamics of
COVID-19 pandemic. medRxiv 20141242. https://doi.org/10.1101/2020.06.26.20141242
111. Bosch J, Wilson A, O’Neil K, Zimmerman PA (2020) COVID-19Predict—predicting
pandemic trends. medRxiv 20191593. https://doi.org/10.1101/2020.09.09.20191593
112. Asad A, Srivastava S, Verma MK (2020) Evolution of COVID-19 pandemic in India.
medRxiv 20143925. https://doi.org/10.1101/2020.07.01.20143925
113. Breen R, Ermisch J (2020) The geography of excess deaths in England during the Covid-19
pandemic: longer term impacts and monthly dynamics. medRxiv 20188003. https://doi.org/
10.1101/2020.09.07.20188003
114. Aries N, Ounis H (2020) Mathematical modeling of COVID-19 pandemic in the African
continent. medRxiv 20210427. https://doi.org/10.1101/2020.10.10.20210427
115. Guenther F, Bender A, Katz K, Kuechenhoff H, Hoehle M (2020) Nowcasting the
COVID-19 pandemic in Bavaria. medRxiv 20140210. https://doi.org/10.1101/2020.06.26.
20140210
116. Althouse BM et al (2020) The unintended consequences of inconsistent pandemic control
policies. medRxiv 20179473. https://doi.org/10.1101/2020.08.21.20179473
117. Yang W, Shaff J, Shaman J (2020) COVID-19 transmission dynamics and effectiveness of
public health interventions in New York City during the 2020 spring pandemic wave.
medRxiv 20190710. https://doi.org/10.1101/2020.09.08.20190710
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