Assessing the effect of lockdown on COVID-19 pandemic through risk prediction model in major cities of India

S Kirubakaran; Balaji Ramraj

doi:10.4103/ijhas.IJHAS_103_20

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Year : 2020 | Volume : 9 | Issue : 5 | Page : 68-72

Assessing the effect of lockdown on COVID-19 pandemic through risk prediction model in major cities of India

S Kirubakaran¹, Balaji Ramraj²
¹ Department of Community Medicine, Government Thiruvarur Medical College and Hospital, Thiruvarur, Tamil Nadu, India
² Department of Community Medicine, SRM Medical College Hospital and Research Centre, Kattankulatur, Tamil Nadu, India

Date of Submission	11-May-2020
Date of Decision	23-May-2020
Date of Acceptance	11-May-2020
Date of Web Publication	04-Jun-2020

Correspondence Address:
Dr. Balaji Ramraj
Department of Community Medicine, SRM Medical College Hospital and Research Centre, Kattankulatur - 603 203, Tamil Nadu
India

Source of Support: None, Conflict of Interest: None

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DOI: 10.4103/ijhas.IJHAS_103_20

Abstract

The World Health Organization declared COVID-19 outbreak as a pandemic on March 11. Models can be established for this process to analyze and study the transmission process of infectious diseases theoretically. This paper presents the prediction of the number of positive COVID-19 cases for different lockdown scenario being implemented in some of the major cities in India. The predictions and assessments were based on a newly developed mathematical model that divides the population into four classes, i.e., susceptible, exposed, infected, and removed. According to the model, total lockdown can produce an effect in the reduction of number of corona cases in the major cities. However, similar difference may not be noted for the entire country as per the prediction.

Keywords: COVID-19, lockdown, risk prediction

How to cite this article:
Kirubakaran S, Ramraj B. Assessing the effect of lockdown on COVID-19 pandemic through risk prediction model in major cities of India. Int J Health Allied Sci 2020;9, Suppl S1:68-72

How to cite this URL:
Kirubakaran S, Ramraj B. Assessing the effect of lockdown on COVID-19 pandemic through risk prediction model in major cities of India. Int J Health Allied Sci [serial online] 2020 [cited 2023 Dec 11];9, Suppl S1:68-72. Available from: https://www.ijhas.in/text.asp?2020/9/5/68/285954

Introduction

The World Health Organization (WHO) declared COVID-19 outbreak as a public health emergency of international concern on January 30, 2020, and declared pandemic on March 11.^[1] Infectious diseases cause catastrophic effect on human society and are one of the important factors that pose as a major threat to health, confine social and economic development, and endanger national security and stability.^[2]

Due to severity of the pandemic, many countries have implemented complete or partial lockdowns and international travel restrictions to stem disease transmission.^[3],[4] Since India reported its first COVID-19-positive patient on January 31, the government has gradually been widening measures to prevent transmission. It is crucial to understand that the transmission dynamics of the infection estimation of changes in transmission over time can provide insights into the epidemiological situation.^[5]

Community-wide containment is an intervention applied to an entire community, city, or region, designed to reduce personal interactions, except for minimal interaction to ensure vital supplies. It is a continuum to expand from social distancing to community-wide quarantine, with major movement restrictions of everyone. Enforcement of community-wide containment measures is far more complex given the larger number of persons involved.^[6]

The Government of India has announced a countrywide lockdown for 3 weeks starting at midnight on March 24, 2020, to slow the spread of COVID-19, and relaxed the lockdown up to May 3 on March 15. The Government of Tamil Nadu also enforced the same effectively throughout the state.^[7]

Models can be established for this process to analyze and study the transmission process of infectious diseases theoretically.^[8] Therefore, to control or reduce the harm of infectious diseases, the research and analysis of infectious disease prediction models have become a hot research topic.^[9] This paper presents the prediction of the number of COVID-19-positive cases for the different lockdown scenario being implemented in some of the major cities in India.

Estimation of the Risk Predication and Assessment of the Dynamics of Covid-19 Cases and Its Transmission in India

This analysis was based on the adaptive interacting cluster-based SEIR (AICSEIR) model proposed by IIT Delhi named as PRediction and Assessment of CoRona Infections and Transmission in India (PRACRITI).^[10] The projections are given for a 3-week period, which is updated on a weekly basis. This includes the effect of movement of population across district/state borders in the COVID-19 transmission.

The predictions and assessments were based on a newly developed mathematical model that divides the population into four classes, i.e., susceptible, exposed, infected, and removed. “Susceptible” refers to people who have not been exposed to the coronavirus, “exposed” refers to those who have been exposed to the virus from an infected person, “infected” refers to those who are actively infected with COVID-19, and “removed” refers to those who are no longer a carrier of the virus.

A key parameter of interest on COVID-19 is the basic reproduction number R0 and its countrywide variability. R0 refers to the number of people to whom the disease spreads from a single infected person. Reduction of R0 is the key in controlling and mitigating the COVID-19 in India. The R0 values of each district and state in India were used from the data available from sources, such as the Ministry of Health and Family Welfare, Government of India; National Disaster Management Authority; and WHO.

Using this model, we attempted to predict the number of cases till May 24, 2020, for the effect of different lockdown scenarios, such as no lockdown, partial lockdown, and total lockdown. Considering various confounding factors, such as administrative interventions, virulence of viral strain, changes in temperature, and community participation, the model will be updated on a weekly basis in an adaptive fashion to account these variations for the accurate predictions.

Risk Prediction Models

[Figure 1] predicts the number of COVID-19 cases in India to 119,822 cases when there is no intervention of lockdown in the country. This prediction reduces to 119,359 cases with partial lockdown and 119,296 cases with total lockdown. Having sustained lockdown as a containment strategy to combat the COVID-19 pandemic has to be given a thought with a minimal difference in the numbers being predicted. However, we could predict a massive increase of cases in the next 3 weeks in the country as a whole, irrespective of the lockdown scenarios. The nation needs to be prepared to combat this number.

Figure 1: Prediction of COVID-19 cases in India under different lockdown scenarios. (a) No lockdown. (b) Partial lockdown. (c) Total lockdown

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[Figure 2] shows the prediction of COVID-19 cases in Chennai under different lockdown scenarios. [Figure 2] predicts the number of COVID-19 cases in Chennai city to 4950 cases when there is no intervention of lockdown in the city. With partial lockdown, it goes to 4869 cases, whereas a good decrease is noted with total lockdown with a prediction of 4565. This reduction in the number with total lockdown can have a profound difference in the future, considering the current basic reproduction number R0 of 1.78 for Chennai city.

Figure 2: Prediction of COVID-19 cases in Chennai under different lockdown scenarios. (a) No lockdown. (b) Partial lockdown. (c) Total lockdown

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[Figure 3] predicts the number of COVID-19 cases in Mumbai city to 6165 cases if lockdown has not been implemented. The partial lockdown can bring this prediction number to 6103 cases, which will be almost similar to no lockdown model. However, total lockdown can show some effect in the number of predicted cases being reduced to 5886. The states of Tamil Nadu and Maharashtra fall in the top five states of the country in relation to number of COVID cases. Their capital cities contributing the major number of cases in the state follow a similar pattern in terms of lockdown scenarios.

Figure 3: Prediction of COVID-19 cases in Mumbai under different lockdown scenarios. (a) No lockdown. (b) Partial lockdown. (c) Total lockdown

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[Figure 4] predicts the number of COVID-19 cases in the capital of the county, Delhi. If the lockdown strategy is not implemented, the prediction is 6601 cases on March 24 in Delhi. With containment strategies such as partial lockdown and total lockdown, the risk prediction is reduced to 6464 cases and 5868 cases, respectively.

Figure 4: Prediction of COVID-19 cases in Delhi under different lockdown scenarios. (a) No lockdown. (b) Partial lockdown. (c) Total lockdown

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With the AICSEIR model, PRACRITI clearly states that total lockdown for the next 3 weeks can produce an effect in the reduction of number of corona cases in the major cities, where the major proportion of cases occur due to various reasons. However, similar difference may not be noted for the entire country as per the prediction. This could be due to the varied social, cultural, and demographic determinants existing in the country.

Conclusion

The risk prediction models seem to be very helpful in predicting the number of cases in the near future and the same can be utilized in framing the strategies to combat the pandemic. With the current prediction, the nation has to anticipate the big surge in cases in the forthcoming weeks. The lockdown strategy is found to be effective in major cities where cluster of cases are reported. However, the prediction suggests the need for planning the exit strategy in the noncontainment zones.

Acknowledgment

We would like to acknowledge the IIT Delhi M3RG Team, namely Mr. Hargun Singh Grover (UG), Mr. Ravinder (PhD), Dr. Amreen Jan (Postdoc), Mr. Sourabh Singh (UG), Mr. Suresh Bishnoi (UG), and Prof. N. M. Anoop Krishnan (PI) who developed the PRACRITI software for risk prediction and transmission dynamics in collaboration with Prof. Hariprasad Kodamana (CAPS, IITD), and Prof. Amit Sharma (ICGEB, New Delhi).

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

References

1.	World Health Organization. Statement on the Second Meeting of the International Health Regulations (2005) Emergency Committee Regarding the Outbreak of Novel Coronavirus (2019-nCov). World Health Organization; 2020.
2.	Yuan DF, Ying LY, Dong CZ. Research progress on epidemic early warning model. Zhejiang Preventive Med 2012;8:20-4.
3.	Chinazzi M, Davis JT, Ajelli M, Gioannini C, Litvinova M, Merler S, et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Sci 2020;368:395-400.
4.	Malta M, Rimoin AW, Strathdee SA. The coronavirus 2019-nCoV epidemic: Is hindsight 20/20? E Clin Med 2020;20:1-2.
5.	Camacho A, Kucharski A, Aki-Sawyerr Y, White MA, Flasche S, Baguelin M, et al. Temporal changes in Ebola transmission in Sierra Leone and implications for control requirements: A real-time modelling study. PLoS Curr 2015;7.
6.	Wilder-Smith A, Freedman DO. Isolation, quarantine, social distancing and community containment: Pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. J Travel Med 2020;27:1-4.
7.	Pulla P. Covid-19: India imposes lockdown for 21 days and cases rise. BMJ 2020;368:m1251.
8.	Grassly NC, Fraser C. Mathematical models of infectious disease transmission. Nature Rev Microbiol 2008;6:477-87.
9.	Yang B, Pei H, Chen H, Liu J, Xia S. Characterizing and discovering spatiotemporal social contact patterns for healthcare. IEEE Trans Pattern Anal Mach Intell 2017;39:1532-46.
10.	Adzerikho RD, Aksentsev SL, Okun' IM, Konev SV. Letter: Change in trypsin sensitivity during structural rearrangements in biological membranes. Biofizika 1975;20:942-4.