COVID-19 in India: Predictions, Reproduction Number and Public Health Preparedness

Rai, Balram; Shukla, Anandi; Dwivedi, Laxmi Kant

doi:10.1101/2020.04.09.20059261

Cited by 20 publications

(24 citation statements)

References 11 publications

(10 reference statements)

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“…(2020) 4.5 Global (across many nations) Katul et al. (2020) 2.56 India Rai et al. (2020) 2.7 (95%CI, 2.1–3.4) Iran Khosravi et al.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the effect of the state of emergency for the first wave of COVID-19 in Japan

Kuniya¹

2020

Infectious Disease Modelling

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In this paper, we evaluate the effect of the state of emergency for the first wave of COVID-19 in Japan, 2020 from the viewpoint of mathematical modelling. In Japan, it was announced during the period of the state of emergency from April 7 to May 25, 2020 that the 80% reduction of the contact rate is needed to control the outbreak. By numerical simulation, we show that the reduction rate seems to have reached up to 86%. Moreover, we estimate the control reproduction number during the period of the state of emergency as (95%CI, 0.34–0.39), and show that the effective reproduction number after the lifting of the state of emergency could be greater than 1. This result suggests us that the second wave of COVID-19 in Japan could possibly occur if any effective intervention will not be taken again.

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“…(2020) 4.5 Global (across many nations) Katul et al. (2020) 2.56 India Rai et al. (2020) 2.7 (95%CI, 2.1–3.4) Iran Khosravi et al.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the effect of the state of emergency for the first wave of COVID-19 in Japan

Kuniya¹

2020

Infectious Disease Modelling

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“…counties). Active COVID-19 cases were based on diagnosed and reported cases of deaths and recoveries; specifically, active cases were calculated by subtracting a given day’s deaths and recoveries from confirmed cases, following the conventional approach used to compute active COVID-19 cases [ 12 ].…”

Section: Methodsmentioning

confidence: 99%

Sub-national longitudinal and geospatial analysis of COVID-19 tweets

Cuomo

Purushothaman

et al. 2020

PLoS ONE

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Objectives According to current reporting, the number of active coronavirus disease 2019 (COVID-19) infections is not evenly distributed, both spatially and temporally. Reported COVID-19 infections may not have properly conveyed the full extent of attention to the pandemic. Furthermore, infection metrics are unlikely to illustrate the full scope of negative consequences of the pandemic and its associated risk to communities. Methods In an effort to better understand the impacts of COVID-19, we concurrently assessed the geospatial and longitudinal distributions of Twitter messages about COVID-19 which were posted between March 3rd and April 13th and compared these results with the number of confirmed cases reported for sub-national levels of the United States. Geospatial hot spot analysis was also conducted to detect geographic areas that might be at elevated risk of spread based on both volume of tweets and number of reported cases. Results Statistically significant aberrations of high numbers of tweets were detected in approximately one-third of US states, most of which had relatively high proportions of rural inhabitants. Geospatial trends toward becoming hotspots for tweets related to COVID-19 were observed for specific rural states in the United States. Discussion Population-adjusted results indicate that rural areas in the U.S. may not have engaged with the COVID-19 topic until later stages of an outbreak. Future studies should explore how this dynamic can inform future outbreak communication and health promotion.

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“…Thus, each infection causes furthermore and the outbreak will continue to grow. The estimated number of R0 in India was 2.56 prior to the "Lockdown 2.0" (30 th April 2020) [4] and it appears that Maharashtra has an elevated R0, as there, growth in the number of COVID-19 victims is intense, particularly, in two of its robust cities, Mumbai and Pune.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the COVID-19 Outbreak: An Application of Arima and Fuzzy Time Series Models

Verma

Khetan

Dwivedi

et al. 2020

Preprint

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Purpose: The whole world is surfaced with an inordinate challenge of mankind due to COVID-19, caused by 2019 novel coronavirus (SARS-CoV-2). After taking hundreds of thousands of lives, millions of people are still in the substantial grasp of this virus. This virus is highly contagious with reproduction number R0, as high as 6.5 worldwide and between 1.5 to 2.6 in India. So, the number of total infections and the number of deaths will get a day-to-day hike until the curve flattens. Under the current circumstances, it becomes inevitable to develop a model, which can anticipate future morbidities, recoveries, and deaths. Methods: We have developed some models based on ARIMA and FUZZY time series methodology for the forecasting of COVID-19 infections, mortalities and recoveries in India and Maharashtra explicitly, which is the most affected state in India, following the COVID-19 statistics till “Lockdown 3.0” (17th May 2020). Results: Both models suggest that there will be an exponential uplift in COVID-19 cases in the near future. We have forecasted the COVID-19 data set for next seven days. The forecasted values are in good agreement with real ones for all six COVID-19 scenarios for Maharashtra and India as a whole as well.Conclusion: The forecasts for the ARIMA and FUZZY time series models will be useful for the policymakers of the health care systems so that the system and the medical personnel can be prepared to combat the pandemic.

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COVID-19 in India: Predictions, Reproduction Number and Public Health Preparedness

Cited by 20 publications

References 11 publications

Evaluation of the effect of the state of emergency for the first wave of COVID-19 in Japan

Evaluation of the effect of the state of emergency for the first wave of COVID-19 in Japan

Sub-national longitudinal and geospatial analysis of COVID-19 tweets

Forecasting the COVID-19 Outbreak: An Application of Arima and Fuzzy Time Series Models

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