Back

Increased Detection coupled with Social Distancing and Health Capacity Planning Reduce the Burden of COVID-19 Cases and Fatalities: A Proof of Concept Study using a Stochastic Computational Simulation Model

Ghosh, P.; Basheer, S.; Paul, S.; Chakrabarti, P.; Sarkar, J.

2020-04-07 public and global health
10.1101/2020.04.05.20054775 medRxiv
Show abstract

ObjectiveIn absence of any vaccine, the Corona Virus Disease 2019 (COVID-19) pandemic is being contained through a non-pharmaceutical measure termed Social Distancing (SD). However, whether SD alone is enough to flatten the epidemic curve is debatable. Using a Stochastic Computational Simulation Model, we investigated the impact of increasing SD, hospital beds and COVID-19 detection rates in preventing COVID-19 cases and fatalities. Research Design and MethodsThe Stochastic Simulation Model was built using the EpiModel package in R. As a proof of concept study, we ran the simulation on Kasaragod, the most affected district in Kerala. We added 3 compartments to the SEIR model to obtain a SEIQHRF (Susceptible-Exposed-Infectious-Quarantined-Hospitalised-Recovered-Fatal) model. ResultsImplementing SD only delayed the appearance of peak prevalence of COVID-19 cases. Doubling of hospital beds couldnt reduce the fatal cases probably due to its overwhelming number compared to the hospital beds. Increasing detection rates could significantly flatten the curve and reduce the peak prevalence of cases (increasing detection rate by 5 times could reduce case number to half). ConclusionsAn effective strategy to contain the epidemic spread of COVID-19 in India is to increase detection rates in combination with SD measures and increase in hospital beds. HIGHLIGHTSO_LIIncreased Detection of COVID-19 cases must accompany Social Distancing and Health Capacity Planning to reduce the burden of cases and fatalities. C_LIO_LIInterruptive Social Distancing is an effective alternative to continuous Social Distancing. C_LIO_LIGiven the overwhelming burden of COVID-19 fatalities, there is immediate need of co-ordination with the Private Healthcare Sector. C_LIO_LICOVID-19 cases will be peaking after May, 2020 giving us time for Healthcare Capacity Building in the government and private sector both. C_LI

Matching journals

The top 12 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.