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An epidemic model SIPHERD and its application for prediction of the spread of COVID-19 infection in India.

ABSTRACT: Originating from Wuhan, China, in late 2019, and with a gradual spread in the last few months, COVID-19 has become a pandemic crossing 9 million confirmed positive cases and 450 thousand deaths. India is not only an overpopulated country but has a high population density as well, and at present, a high-risk nation where COVID-19 infection can go out of control. In this paper, we employ a compartmental epidemic model SIPHERD for COVID-19 and predict the total number of confirmed, active and death cases, and daily new cases. We analyze the impact of lockdown and the number of tests conducted per day on the prediction and bring out the scenarios in which the infection can be controlled faster. Our findings indicate that increasing the tests per day at a rapid pace (10k per day increase), stringent measures on social-distancing for the coming months and strict lockdown in the month of July all have a significant impact on the disease spread.

SUBMITTER: Mahajan A

PROVIDER: S-EPMC7386481 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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An epidemic model SIPHERD and its application for prediction of the spread of COVID-19 infection in India.

Mahajan Ashutosh A Sivadas Namitha A NA Solanki Ravi R

Chaos, solitons, and fractals 20200728

Originating from Wuhan, China, in late 2019, and with a gradual spread in the last few months, COVID-19 has become a pandemic crossing 9 million confirmed positive cases and 450 thousand deaths. India is not only an overpopulated country but has a high population density as well, and at present, a high-risk nation where COVID-19 infection can go out of control. In this paper, we employ a compartmental epidemic model SIPHERD for COVID-19 and predict the total number of confirmed, active and death ...[more]

PMID: 32834644

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Project description:ObjectiveThe Coronavirus Disease 2019 (COVID-19) has currently ravaged through the world, resulting in over thirteen million confirmed cases and over five hundred thousand deaths, a complete change in daily life as we know it, worldwide lockdowns, travel restrictions, as well as heightened hygiene measures and physical distancing. Being able to analyse and predict the spread of this epidemic-causing disease is hence of utmost importance now, especially as it would help in the reasoning behind important decisions drastically affecting countries and their people, as well as in ensuring efficient resource and utility management. However, the needs of the people and specific conditions of the spread are varying widely from country to country. Hence, this article has two fold objectives: (i) conduct an in-depth statistical analysis of COVID-19 affected patients in India, (ii) propose a mathematical model for the prediction of spread of COVID-19 cases in India.Materials and methodThere has been limited research in modeling and predicting the spread of COVID-19 in India, owing both to the ongoing nature of the pandemic and limited availability of data. Currently famous SIR and non-SIR based Gauss-error-function and Monte Carlo simulation models do not perform well in the context of COVID-19 spread in India. We propose a 'change-factor' or 'rate-of-change' based mathematical model to predict the spread of the pandemic in India, with data drawn from hundreds of sources.ResultsAverage age of affected patients was found to be 38.54 years, with 66.76% males, and 33.24% females. Most patients were in the age range of 18-40 years. Optimal parameter values of the prediction model are identified (α = 1.35, N = 3 and T = 10) by extensive experiments. Over the entire course of time since the outbreak started in India, the model has been 90.36% accurate in predicting the total number of cases the next day, correctly predicting the range in 150 out of the 166 days looked at.ConclusionThe proposed system showed an accuracy of 90.36% for prediction since the first COVID-19 case in India, and 96.67% accuracy over the month of April. Predicted number of cases for the next day is found to be a function of the numbers over the last 3 days, but with an 'increase' factor influenced by the last 10 days. It is noticed that males are affected more than females. It is also noticed that in India, the number of people in each age bucket is steadily decreasing, with the largest number of adults infected being the youngest ones-a departure from the world trend. The model is self-correcting as it improves its predictions every day, by incorporating the previous day's data into the trend-line for the following days. This model can thus be used dynamically not only to predict the spread of COVID-19 in India, but also to check the effect of various government measures in a short span of time after they are implemented.

Project description:BACKGROUND:The highly infectious coronavirus disease (COVID-19) was first detected in Wuhan, China in December 2019 and subsequently spread to 212 countries and territories around the world, infecting millions of people. In India, a large country of about 1.3 billion people, the disease was first detected on January 30, 2020, in a student returning from Wuhan. The total number of confirmed infections in India as of May 3, 2020, is more than 37,000 and is currently growing fast. OBJECTIVE:Most of the prior research and media coverage focused on the number of infections in the entire country. However, given the size and diversity of India, it is important to look at the spread of the disease in each state separately, wherein the situations are quite different. In this paper, we aim to analyze data on the number of infected people in each Indian state (restricted to only those states with enough data for prediction) and predict the number of infections for that state in the next 30 days. We hope that such statewise predictions would help the state governments better channelize their limited health care resources. METHODS:Since predictions from any one model can potentially be misleading, we considered three growth models, namely, the logistic, the exponential, and the susceptible-infectious-susceptible models, and finally developed a data-driven ensemble of predictions from the logistic and the exponential models using functions of the model-free maximum daily infection rate (DIR) over the last 2 weeks (a measure of recent trend) as weights. The DIR is used to measure the success of the nationwide lockdown. We jointly interpreted the results from all models along with the recent DIR values for each state and categorized the states as severe, moderate, or controlled. RESULTS:We found that 7 states, namely, Maharashtra, Delhi, Gujarat, Madhya Pradesh, Andhra Pradesh, Uttar Pradesh, and West Bengal are in the severe category. Among the remaining states, Tamil Nadu, Rajasthan, Punjab, and Bihar are in the moderate category, whereas Kerala, Haryana, Jammu and Kashmir, Karnataka, and Telangana are in the controlled category. We also tabulated actual predicted numbers from various models for each state. All the R2 values corresponding to the logistic and the exponential models are above 0.90, indicating a reasonable goodness of fit. We also provide a web application to see the forecast based on recent data that is updated regularly. CONCLUSIONS:States with nondecreasing DIR values need to immediately ramp up the preventive measures to combat the COVID-19 pandemic. On the other hand, the states with decreasing DIR can maintain the same status to see the DIR slowly become zero or negative for a consecutive 14 days to be able to declare the end of the pandemic.

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An epidemic model SIPHERD and its application for prediction of the spread of COVID-19 infection in India.

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An epidemic model SIPHERD and its application for prediction of the spread of COVID-19 infection in India.

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