Extended SIR Prediction of the Epidemics Trend of COVID-19 in Italy and Compared With Hunan, China

Jia, Wenyu; Han, Kihye; Song, Yang; Cao, Wenzhe; Wang, Shengshu; Yang, Shanshan; Wang, Jianwei; Kou, Fuyin; Tai, Penggang; Li, Jing; Liu, Miaomiao; He, Yao

doi:10.3389/fmed.2020.00169

Cited by 182 publications

(106 citation statements)

References 34 publications

(32 reference statements)

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“…Across all US states, the maximal ℛ values of were estimated for New York (4.4) and Michigan (4.5) ( Table 1) which is close to the mean value of 4.34 estimated for Italy (Wangping et al, 2020) but higher than that obtained by a stochastic transmission model (Abbott et al, 2020;Kucharski et al, 2020). The wide range of maximal values of ℛ from 2.0 to 4.5 (Table 1) likely reflects the differences in contact rates due to population density (H. Hu et al, 2013;Sy et al, 2020).…”

Section: Discussionsupporting

confidence: 66%

Are we there yet? An adaptive SIR model for continuous estimation of COVID-19 infection rate and reproduction number in the United States

Karim

Muscioni

et al. 2020

Preprint

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The dynamics of the COVID-19 epidemic vary due to local population density and policy measures. When making decisions, policy makers consider an estimate of the effective reproduction number R_t which is the expected number of secondary infections by a single infected individual. We propose a simple method for estimating the time-varying infection rate and reproduction number R_t using a sliding window approach applied to a Susceptible-Infectious-Removed model. The infection rate is estimated using the reported cases for a seven-day window to obtain continuous estimation of R_t. We demonstrate that the proposed adaptive SIR (aSIR) model can quickly adapt to an increase in the number of tests and associated increase in the reported cases of infections. Our results also suggest that intensive testing may be one of the effective methods of reducing R_t. The aSIR model was applied to data at the state and county levels.

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Section: Discussionsupporting

confidence: 66%

Are we there yet? An adaptive SIR model for continuous estimation of COVID-19 infection rate and reproduction number in the United States

Karim

Muscioni

et al. 2020

Preprint

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“…Epidemiological models have been widely used to make predictions about the evolution of the COVID-19 epidemics, to predict the outcome of testing strategies and intervention scenarios, and to estimate epidemiological parameters [4][5][6][7][8]. Epidemic spread is determined by both biological properties of the virus as well as the behavior of the host population, and this is reflected in the epidemiological parameters.…”

Section: Introductionmentioning

confidence: 99%

Behavioral changes before lockdown and decreased retail and recreation mobility during lockdown contributed most to controlling COVID-19 in Western countries

Deforche

Vercauteren

Müller

et al. 2020

Preprint

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BackgroundThe COVID-19 pandemic has prompted a lockdown in many countries to control the exponential spread of the SARS-CoV-2 virus, hereby reducing the time-varying basic reproduction number ( R t ) to below one. Governments are looking for evidence to balance the demand of their citizens to ease some of the restriction, against the fear of a second peak in infections. More details on the specific circumstances that promote exponential spread (i.e. R t > 1) are needed.MethodsIncidence data of cases and deaths from the first wave of infections for 35 Western countries (32 European, plus Israel, USA and Canada) were analyzed using epidemiological compartment models in a Bayesian framework. Mobility data was used to estimate the timing of changes associated with a lockdown, and was correlated with estimated reductions of R t .ResultsAcross all countries, the initial median estimate for R t was 3.6 (95% IQR 2.4 – 5.2), and it was reduced to 0.78 (95% IQR 0.58 – 1.01) during lockdown. 48% (18% – 65%) of the reduction occurred already in the week before lockdown, with lockdown itself causing the remaining drop in transmission. A lower R t during lockdown was independently associated with an increased time spent at home (0.21 per 10% more time, p < 0.007), and decreased mobility related to retail and recreation (0.07 per 10% less mobility, p < 0.008).ConclusionsIn a Western population unaware of the risk, SARS-CoV-2 can be highly contagious with a reproduction number R 0 > 5. Our results are consistent with evidence that recreational activities (including restaurant and bar visits) enable super-spreading events. Exiting from lockdown therefore requires continued physical distancing and tight control on this kind of activities.

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“…The SIR model [20,21] is one of the olden compartmental models in epidemiology projecting infectious diseases like COVID-19 [22,23,24,25,26,27] and numerous diversified derivations came out from it. The principal SIR model comprises three compartments.…”

Section: Prediction Models and Their Componentsmentioning

confidence: 99%

4P Model for Dynamic Prediction of Covid-19 with statistical and machine learning approach: Social Dynamics, Government Roles and Pathogen Behavior on Bangladesh Perspective

Hasan

Ahmmed

Chowdhury

et al. 2020

Preprint

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Introduction: Around the world, scientists are racing hard to understand how Covid-19 epidemic is spreading and growing, thus trying to find ways to prevent it before medications come to pass. Many different models have been proposed so far correlating different factors. Some of them are too localized to indicate a general trend of the pandemic while some others have established transient correlations only.Methods: Hence, in this study, a 4P model has been proposed based on four probabilities (4P) which has been found to be true for all affected countries taking Bangladesh as a case. Efficiency scores have been estimated from survey analysis not only for governing authorities on managing the situation (P(G)) but also for the compliance of the citizens ((P(P)). Since the immune responses of all the people are not uniform to a specific pathogen, the probability of a person getting infected ((P(I)) after being exposed has also been estimated. And the vital one is the probability of Test Positivity ((P(T)) which is a strong indicator of how effectively the infected people are diagnosed and isolated from the rest of the group that affects the rate of growth.Results and Conclusion: All the four parameters have been fitted in a non-linear exponential model that partly updates itself periodically with everyday facts. Along with the model, all the four probabilistic parameters are engaged to train a recurrent neural network using Long-Short Term Memory neural network and the followed trial confirmed a ruling functionality of the 4Ps.

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Extended SIR Prediction of the Epidemics Trend of COVID-19 in Italy and Compared With Hunan, China

Cited by 182 publications

References 34 publications

Are we there yet? An adaptive SIR model for continuous estimation of COVID-19 infection rate and reproduction number in the United States

Are we there yet? An adaptive SIR model for continuous estimation of COVID-19 infection rate and reproduction number in the United States

Behavioral changes before lockdown and decreased retail and recreation mobility during lockdown contributed most to controlling COVID-19 in Western countries

4P Model for Dynamic Prediction of Covid-19 with statistical and machine learning approach: Social Dynamics, Government Roles and Pathogen Behavior on Bangladesh Perspective

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