Modelling the epidemiological trend and behavior of COVID-19 in Italy

Rovetta, Alessandro; Bhagavathula, Akshaya Srikanth

doi:10.1101/2020.03.19.20038968

Cited by 16 publications

(17 citation statements)

References 21 publications

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“…15 ► Several papers were removed since they contained insufficient data or method description to facilitate their inclusion: -One study was removed since there was not enough detail in the paper to determine whether new parameters were being estimated or whether the parameters quoted were input values for their model. 16 -Seven papers were removed since the data were largely descriptive, with no CIs reported. 17-23 -One study was removed because the error terms associated with the mean, median and percentiles were not reported and there was not enough information presented to recover the parameters of the lognormal distribution.…”

Section: Resultsmentioning

confidence: 99%

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Incubation period of COVID-19: a rapid systematic review and meta-analysis of observational research

et al. 2020

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ObjectivesThe aim of this study was to conduct a rapid systematic review and meta-analysis of estimates of the incubation period of COVID-19.DesignRapid systematic review and meta-analysis of observational research.SettingInternational studies on incubation period of COVID-19.ParticipantsSearches were carried out in PubMed, Google Scholar, Embase, Cochrane Library as well as the preprint servers MedRxiv and BioRxiv. Studies were selected for meta-analysis if they reported either the parameters and CIs of the distributions fit to the data, or sufficient information to facilitate calculation of those values. After initial eligibility screening, 24 studies were selected for initial review, nine of these were shortlisted for meta-analysis. Final estimates are from meta-analysis of eight studies.Primary outcome measuresParameters of a lognormal distribution of incubation periods.ResultsThe incubation period distribution may be modelled with a lognormal distribution with pooled mu and sigma parameters (95% CIs) of 1.63 (95% CI 1.51 to 1.75) and 0.50 (95% CI 0.46 to 0.55), respectively. The corresponding mean (95% CIs) was 5.8 (95% CI 5.0 to 6.7) days. It should be noted that uncertainty increases towards the tail of the distribution: the pooled parameter estimates (95% CIs) resulted in a median incubation period of 5.1 (95% CI 4.5 to 5.8) days, whereas the 95th percentile was 11.7 (95% CI 9.7 to 14.2) days.ConclusionsThe choice of which parameter values are adopted will depend on how the information is used, the associated risks and the perceived consequences of decisions to be taken. These recommendations will need to be revisited once further relevant information becomes available. Accordingly, we present an R Shiny app that facilitates updating these estimates as new data become available.

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Section: Resultsmentioning

confidence: 99%

“…One study was removed since there was not enough detail in the paper to determine whether new parameters were being estimated or whether the parameters quoted were input values for their model. 16 …”

Section: Resultsmentioning

confidence: 99%

Incubation period of COVID-19: a rapid systematic review and meta-analysis of observational research

et al. 2020

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“…Mathematical models are widely used to forecast the spreading of the disease and capture the probability of cases from susceptible to infected, and then to a recovery state or death. Many SIR models have been published or proposed online [2][3][4][5]. However, these models assume randomly mixed between all individuals in the given population.…”

Section: Introductionmentioning

confidence: 99%

Forecasting COVID-19 impact in India using pandemic waves Nonlinear Growth Models

Kumar

Singh

Nanda

et al. 2020

Preprint

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The ongoing pandemic of the coronavirus disease 2019 started in China and devastated a vast majority of countries. In India, COVID-19 cases are steadily increasing since January 30, 2020, and the government-imposed lockdown across the country to curtail community transmission. COVID-19 forecasts have played an important role in capturing the probability of infection and the basic reproduction rate. In this study, we predicted some trajectories of trajectories associated with COVID-19 in the coming days in India using an Autoregression integrated moving average model (ARIMA) and Richard's model. By the end of April 2020, the incidence of new cases is predicted to be 5200 (95% CI: 4650 to 6002) through the ARIMA model versus be 6378 (95% CI: 4904 to 7851) Richard model. We estimated that there would be a total of 197 (95% CI: 118 to 277) deaths and drop down in the recovery rates will reach around 501 (95% CI: 245 to 758) by the end of April 2020. These estimates can help to strengthen the implementation of strategies to increase the health system capacity and enactment of social distancing measures all over India.

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“…Such nationwide forecasts are not easily transferable/ scalable to the level needed by the regional authorities and/ or health services in India, who has to make critical decisions on mitigating measures and resource allocation in the light of unprecedented COVID-19 pressures [23], [25]. Here, local, short-term forecasts for example contribute to the strategic planning for coping with the increased hospital needs due to COVID-19 [26], [27].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Statistical Forecasts of COVID-19 Infections in India

Singh

Drews²,

Sen

et al. 2020

IEEE Access

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COVID-19 cases in India have been steadily increasing since January 30, 2020 and have led to a government-imposed lockdown across the country to curtail community transmission with significant impacts on societal systems. Forecasts using mathematical-epidemiological models have played and continue to play an important role in assessing the probability of COVID-19 infection under specific conditions and are urgently needed to prepare health systems for coping with this pandemic. In many instances, however, access to dedicated and updated information, in particular at regional administrative levels, is surprisingly scarce considering its evident importance and provides a hindrance for the implementation of sustainable coping strategies. Here we demonstrate the performance of an easily transferable statistical model based on the classic Holt-Winters method as means of providing COVID-19 forecasts for India at different administrative levels. Based on daily time series of accumulated infections, active infections and deaths, we use our statistical model to provide 48-days forecasts (28 September to 15 November 2020) of these quantities in India, assuming little or no change in national coping strategies. Using these results alongside a complementary SIR model, we find that one-third of the Indian population could eventually be infected by COVID-19, and that a complete recovery from COVID-19 will happen only after an estimated 450 days from January 2020. Further, our SIR model suggests that the pandemic is likely to peak in India during the first week of November 2020.

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Modelling the epidemiological trend and behavior of COVID-19 in Italy

Cited by 16 publications

References 21 publications

Incubation period of COVID-19: a rapid systematic review and meta-analysis of observational research

Incubation period of COVID-19: a rapid systematic review and meta-analysis of observational research

Forecasting COVID-19 impact in India using pandemic waves Nonlinear Growth Models

Short-Term Statistical Forecasts of COVID-19 Infections in India

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