Indian Journal of Public Health

: 2020  |  Volume : 64  |  Issue : 6  |  Page : 156--167

Effectiveness of preventive measures against COVID-19: A systematic review of In Silico modeling studies in indian context

Arista Lahiri1, Sweety Suman Jha2, Saikat Bhattacharya3, Soumalya Ray4, Arup Chakraborty5,  
1 Senior Resident, Department of Community Medicine, College of Medicine and Sagore Dutta Hospital, Kamarhati, India
2 Junior Resident, Department of Preventive and Social Medicine, All India Institute of Hygiene and Public Health, Kolkata, West Bengal, India
3 Assistant Professor, Department of Community Medicine, Medical College and Hospital, Kolkata, West Bengal, India
4 Assistant Professor, Department of Community Medicine, College of Medicine and Sagore Dutta Hospital, Kamarhati, India
5 Associate Professor, Department of Community Medicine, Medical College and Hospital, Kolkata, West Bengal, India

Correspondence Address:
Soumalya Ray
Department of Community Medicine, College of Medicine and Sagore Dutta Hospital, Kamarhati, West Bengal


Background: In the absence of any approved treatment or vaccine against novel Severe Acute Respiratory Syndrome Coronavirus -2 (SARS-CoV-2) infection, Non-Pharmaceutical Interventions (NPIs) are the cornerstone to prevent the disease, especially in a populous country like India. Objectives: To understand the effectiveness of NPIs reported in the contemporary literatures describing prediction models for prevention of the ongoing pandemic of SARS-CoV-2 specifically in Indian population. Methods: Original research articles in English obtained through keyword search in PubMed, WHO Global Database for COVID19, and pre-print servers were included in the review. Thematic synthesis of extracted data from articles were done. Results: Twenty-four articles were found eligible for the review - four published articles and twenty pre-print articles. Compartmental model was found to be the most commonly used mathematical model; along with exponential, time varying, neural network and cluster kinetic models. Social distancing, specifically lockdown, was the most commonly modelled intervention strategy. Additionally, contact tracing using smartphone application, international travel restriction, increasing hospital/ICU beds, changes in testing strategy were also dealt with. Social distancing along with increasing testing seemed to be effective in delaying the peak of the epidemic and reducing the peak prevalence. Conclusion: Although there is mathematical rationality behind implementation of social distancing measures including lockdown, this study also emphasised the importance of other associated measures like increasing tests and increasing the number of hospital and ICU beds. The later components are particularly important during the social mixing period to be observed after lifting of lockdown.

How to cite this article:
Lahiri A, Jha SS, Bhattacharya S, Ray S, Chakraborty A. Effectiveness of preventive measures against COVID-19: A systematic review of In Silico modeling studies in indian context.Indian J Public Health 2020;64:156-167

How to cite this URL:
Lahiri A, Jha SS, Bhattacharya S, Ray S, Chakraborty A. Effectiveness of preventive measures against COVID-19: A systematic review of In Silico modeling studies in indian context. Indian J Public Health [serial online] 2020 [cited 2021 May 10 ];64:156-167
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Full Text


While the world combats the pandemic of severe acute respiratory syndrome coronavirus-2 (SARS-Cov-2 also known as COVID-19), various nonpharmaceutical interventions (NPIs) such as hand-washing, cough etiquette, wearing mask, physical distancing, and isolation have established their relevance in preventing the spread.[1] While elders and comorbid individuals often suffer a serious fate, most of the infected remain asymptomatic or suffer mild disease,[2] which adds to the need statement for proper implementation of NPIs. Globally, different countries have used different prevention models primarily synchronizing different NPIs. In India, along with promotion of personal hygiene, lockdown has been imposed;[3] however, the evidence of effectiveness remains stochastic in nature.

In scenarios, where less amount of data prevent to come to a definite conclusion, mathematical and computational approaches (e.g., compartment models, exponential models, and time series forecasting) provide support for decision-making.[4] In fact, different mathematical models are applied to predict the effectiveness of various public health intervention (nonpharmaceutical) in epidemic situations.[5],[6] Assumptions, being the backbone of the conceptualized circumstance, are cardinal to all these models, as they may lead to a wide variability in outcome numbers and thereby recommendations may be varied.[7],[8] These variations may be even more profound in Indian context, considering the demographic and sociocultural variability.

The forecasting problems are essentially constituted of two layers: first, the pattern of disease without intervention and second, when intervention is layered on it.[9],[10] Thus, conceptually, forecasting the effect of NPIs in Indian context entails the risk of variability at these two levels with a multiplicative effect, yielding even more diverse results. Till date, to the authors' knowledge, there is no such systematic review focusing on these issues in Indian context. The current synthesis, therefore, is a novel attempt providing a calibrated understanding of the effects of NPIs in Indian context. Thus, the aim of this systematic review was to identify the different public health interventions (NPIs) and to understand their proposed effectiveness (as per prediction models), under different assumptions, among Indian population.

 Materials and Methods

No standardized reporting format for systematic reviews of simulation and/or mathematical modeling studies was available focusing on infectious disease modeling. The TRACE guideline[11] highlights the concepts in evaluating the epidemiological prediction models, though also focuses on data-driven validation, which may not be feasible when modeling an epidemic at a very early stage or an epidemic caused by a novel agent. The CHARMS checklist provides a guideline regarding the topics to extract from individual studies for systematic review of prediction models.[12] For reporting of systematic reviews and meta-analysis of interventional epidemiological studies, the PRISMA guideline is considered as the standard.[13] All the three guidelines were taken into account for the current article, considering the complexity of reporting systematic review of prediction models elucidating the effects of public health interventions to control the pandemic in a simulated setting.

Eligibility criteria

Studies discussing prediction models regarding SARS-Cov-2 pandemic in India were considered for the review. Only scientific articles in English language were reviewed. Published articles including ahead of print articles were included along with available preprint articles, identified through systematic literature search. Review articles, case reports, and conference reports were excluded. The articles which did not seek to model the effect of any of the public health interventions on future trends of COVID-19 disease in India were also excluded. A thorough literature search was conducted for all the available scientific articles of the mentioned categories on the topic till April 21, 2020. Considering the rapid increase in publications globally regarding the issue of COVID-19 and its predictions, the search was updated on April 25, 2020.

Search strategy

For article search, PubMed database and WHO Global Database for COVID-19 were accessed along with arXiv, medRxiv, and bioRxiv preprint servers. For PubMed search, the keywords along with Boolean operators were as follows: < “Prediction” OR “Predictive” OR “Forecast” OR “Forecasting” OR “Simulation” OR “Model” OR “Modeling” OR “Mathematical” OR “Compartmental” OR “Compartment” > AND < “COVID19” OR “SARS-CoV-2” OR “Coronavirus” OR “severe acute respiratory syndrome coronavirus 2” > AND < “India” >. For WHO Global Database for COVID-19, the keywords were < “Prediction” OR “Forecast” OR “Simulation” OR “Model” OR “Modeling” OR “Mathematical” OR “Compartmental” OR “Compartment” > AND < “India” >. In case of the preprint servers, a similar keyword search was conducted. Search was performed with keywords focusing on or similar to “COVID,” “Prediction,” and “Model.” The articles finally included in the review were examined for cross-references in order to include additional article not identified through the mentioned search protocol. However, examining the cross-references did not yield any new article matching the eligibility, inclusion, and exclusion criteria.

Obtaining data for qualitative synthesis

The articles were searched by the two researchers and the data from the selected articles were extracted by another two researchers using a semi-structured data extraction form. The data extraction form was created specifically for the purpose of the current research. The form sought information on objective/problem statement, setting for modeling, i.e., whole of India or any specific region/state, names of different interventions modeled, modeling framework, assumptions utilized for each model, model fitness/validation information, findings pertinent to each intervention in a study, and conclusion/recommendation. Risk of bias for the articles was not assessed separately, because it was assumed that assumptions in these models would invariably incur some degree of noise in the predictions. The specific reason behind such liberal appraisal was the unavailability of standardized measures for the models in India-specific setting.

Synthesis of results

The data extracted were subjected to quantitative and qualitative analyses. Different interventions reported in the reviewed studies were identified qualitatively. The number of studies conducted on a particular type of intervention was also identified. The predictions models used in the articles were assessed. Whether a model was data driven (based on outcome variable data) or simulated (based on input variable data) was also noted. It was noted whether any article used two different models in an article and used one to validate the other. Thematic analysis of the findings and the conclusions were done. Themes were generated for the public health interventions in question. Mathematical values from the articles were put forward as evidence to support or oppose a particular intervention. Quality of evidence generated in hypothetical situations in these studies was assessed conservatively through assessment of applicability of the models and validity statements. The limitations declared in these studies were taken into account when synthesizing the key findings.


Search results and study characteristics

Following the search strategy outlined previously, 111 articles were first identified from the database searches. After removing duplicates and screening the remaining articles by their title and abstract, 27 articles were included for full-text analysis. Three full-text articles were excluded because these articles in their models did not provide any estimate of the effects of any of the public health interventions in controlling COVID-19 situation in India. Of the remaining 24 articles included for this systematic review, four articles were published articles and the remaining 20 articles were preprints, i.e., were not peer-reviewed as yet. These studies nested their models and predictions among the Indian population, though not always considering the demographic distribution of the population. On the other hand, a preprint authored by Singh and Adhikari[14] took into account the age and sex structure of the population but like in other studies did not consider the natural birth or death rate. The article selection process is depicted in [Figure 1].{Figure 1}

Evaluation of prediction models

[Table 1] summarizes the methodological aspects related to the studies reviewed. The most frequent model framework observed was SEIR framework. The SEIR framework was modified by authors to fit into their scopes. SEIR model was most often augmented with a quarantined compartment.[15],[16],[17] However, authors of pre-prints have extended the SEIR model to add quarantine, asymptomatic, fatal compartments as well.[18],[19] SIR compartmental model was also used. Singh and Adhikari[14] in their study utilized the SIR framework but with age-structuring. Exponential models and time varying models also used. Neural network models were used for prediction purpose in a data-driven mode by Tomar and Gupta.[20] Kishore et al.[21] in his article proposed a cluster kinetic model to account for physical distancing among clusters. Adding to the large variation of models is a branching chain model used by Bulchandani et al.,[22] while describing the basis and effectiveness of a contact-tracing app.{Table 1}

From [Table 1], it is noted that statement of model validation is not always associated with an article/preprint. Most commonly, the fitness information related to least square regression methodology was found. Other simulations were based on specific assumptions; however, model performance through sensitivity analysis was not available always when dealing with compartment models. Furthermore, these model assumptions more often make a perfect model to not fit in the real-world situation. In a preprint, Ranjan[23] therefore stated that the results from the paper should be used only for qualitative understanding and reasonable estimate of the nature of outbreak.

Summary of the findings from the prediction models

Chatterjee et al.[15] concluded that their mathematical model shows that, unchecked, the epidemic is likely to cross 3 million cases by May 25, 2020 and overwhelms the available health-care resources. The authors, thus, emphasized on immediate implementation of NPIs among the general population. They also mentioned that measures such as complete lockdowns have shown to have the potential to retard the progress of the epidemic by April 2020 and reduce the COVID-19 cases, bringing down hospitalizations, intensive care unit requirements, and mortality by almost 90%. In a similar note, Ghosal et al.[24] also stated the need for urgent interventions, to prevent the drastic and sharp rise in death rates which indirectly also indicates an increase in infection rate. The results from the prediction models are summarized in [Table 2].{Table 2}

Effect of port-of-entry screening

Mandal et al.[16] reported that port-of-entry-based screening of travellers with suggestive clinical features and from COVID-19-affected countries, would achieve modest delays in the introduction of the virus into the community. Once community transmission starts quarantine of symptomatic individuals might have a meaningful impact, provided a symptomatic person is quarantined at the earliest preferably within three days. Mandal et al.[16] commented that multiple containment methods may be required to retard the community spread, when it begins. A preprint by Senapati et al.[18] revealed that higher intervention effort is required to control the disease outbreak within a shorter period of time. Also, the strength of the intervention should not be relaxed over the time rather the intervention should be strengthened to eradicate thedisease effectively.

Effect of social distancing and lockdown

Lockdown, a drastic yet theoretically effective distancing strategy enforced by the Government of India, has been modelled for effectiveness by Tomar and Gupta.[20] They found out that in India, value of effective reproduction number for COVID-19 before lockdown was 2.3 and after lockdown it may be reduced to 0.15. In a pre-print Mandal and Mandal,[25] Kishore et al.[21] suggested that the adoption of social distancing measures in order to shrink the peak intensity of outbreaks, wherein the lifting of the measures results in resurgence of SARS-CoV-2 transmission. Though not mathematically tested, yet intuitively the authors emphasize on regular surveillance of SARS-CoV-2 in an urgent manner for the vigilance of further plausible resurgence of COVID-19 outbreaks.

Ranjan[23] found out that in India the effect of lockdown was supposed to be seen after April 8th, 2020. In another pre-print Mishra and Mishra[26] suggested that successful execution of 21-day lockdown in India may save approximately 5,500 lives by April 16. Ray et al.[27] recommended that from an epidemiological perspective, a longer lockdown between 42-56 days is preferable. A different perspective has been presented by Mazumder et al.[28] Their pre-print of mathematical model predicts India would have adequate number of active cases after the 21 days lockdown. Shah[29] proposed that from this work, considering the lockdown the COVID-19 epidemic peak could easily reach August, 2020. Jindal[30] predicted a favorable trajectory for the disease when lockdown is lifted.

An alternative lockdown strategy can be found from the pre-print by Singh and Adhikari.[14] They concluded that the three-week lockdown would be insufficient and suggests sustained periods of lockdown with periodic relaxation which would reduce the number of cases. Das et al.[31] while providing basis for the lockdown measure recommended that till the end of expected time to extinction of the disease careful vigilance is needed. They also cautioned that their model indicates another rise in infection of lesser magnitude a year later.

However, in a recent preprint, Sardar et al.[32] stated that lock-down in Maharashtra would not help its current COVID-19 situation. 21-day lockdown would be effective in Delhi and should be extended further for more cases and deaths reduction. For Delhi and Tamil Nadu, there is some ray of hope as the prediction shows that lockdown would reduce significant number of notified cases and deaths in these two locations. Dhanwant and Ramanathan[33] suggested that social distancing is the only proven solution for halting corona virus growth as of now; however, even during the lockdown, there may not be any decrease in the number of cases.

Effect of combination of interventions

Preprints by Paul et al.[34] and Pant et al.[35] revealed that the lockdown and recommended individual hygiene could slow down the outbreak but unable to eradicate the disease from the society. Ghosh et al.[19] suggested in their preprint that an effective strategy to contain the epidemic spread of COVID-19 in India is to increase detection rates in combination with social distancing measures and increase in hospital beds. Mukhopadhyay and Chakraborty[36] indicated from the data that lockdown and increase in testing have lowered or slowed down the rate of rise in the number of undetected cases. They firmly concluded that lockdown coupled with increased testing has been an effective measure in reducing the rise in infections from COVID-19 in India.

Bhola et al.[37] suggested that the country is going to see a surge in the cases, with the fact that hygiene, physical distancing, staying indoors, and boosting immune system can only flatten the curve. In consonance, Venkateswaran and Damani[17] reported a wide range of combination of effectiveness of contact tracing, isolation, quarantining, and personal hygiene measures that may help minimize the pandemic impact. The authors put forward an interesting observation that some imperfections in the implementation of one measure could be compensated by better implementation of other measures.

A novel method of intensified contact-tracing has been proposed by Bulchandani et al.[22] The author reported that, if the app take-up is between 75% and 95%, effective contact-tracing and thereby path of infection spread for COVID can be obtained. “Digital herd immunity” a term coined by the author can be conceptualized in this regard.


Summary of evidence

Dealing with NPIs, most of the studies reviewed the effect of social distancing methodology. The most common social distancing strategy modeled was lockdown, often making it synonymous with social distancing itself. Some of the studies however answered the probable impact of using multiple interventions in a combined way. Strong evidence was presented in favor of increasing detection or testing for the disease.[19] The article by Mandal et al.[16] in fact initiated the modeling works regarding interventions in India. The modeling, done at the early stages of epidemic in India, presented evidence for port-of-entry screening and quarantine of symptomatic in epidemic control. While in community transmission phase, the later articles focused on hygiene practices and contact tracing as well along with lockdown and testing strategies.[22],[34],[35] While there is evidence supporting an upcoming peak of the disease in July to August, 2020, some authors are however of the opinion that the epidemic will die down in 5 months from its inception. Amidst such variability, the only conclusive evidence that can be garnered in Indian perspective is the effectiveness of social distancing, testing, and personal protection. The differences in model outputs can be attributed to the variability in assumptions of the model parameters and also the use of different models. Despite the lack of sufficient data to advocate firm predictions, the stochastic models based on simulation and imputation techniques do a decent job in providing a qualitative impression about utility of the different measures in Indian context.

Interpretations in the light of other relevant literatures

It has been observed that, while globally data science has taken a step forward in shaping the prediction models during the ongoing pandemic, there is a scarcity of reviews on the public health interventions. A rapid review on modeling studies on COVID-19 stated that instead of a single intervention, combination of quarantine with other public health interventions may serve as better way of controlling the epidemic.[38] The current article supports this finding. Interestingly, the current research did not find any modeling study that emphasized or at least measured the effect of school closure on control of the epidemic in India. The reason may be because school closure in India started at the early stages of the epidemic in India and even when there is discussion on whether to lift the lockdown or not, the school closure unanimously continues till the beginning of June 2020. Thus, keeping schools closed has become an administrative decision beyond any experimentation. However, a rapid systematic review by Viner et al.[39] revealed that school closures alone would prevent only 2%–4% of deaths, much less than other social distancing interventions. Thus, the need for modeling school closure may be already out of question.

In Indian context, a review article summarized different mathematical models related to COVID-19.[40] The authors included web reports in their study. They concluded that the findings could not be synthesized into generalization. On the contrary, the current systematic review yielded a qualitative generalization on the effects of the different interventions. Although limitations of simulation studies were encountered as a hurdle when synthesizing the evidences in the current research, still the qualitative effect of interventions such as lockdown, testing, and hygiene practices could definitively be stated.

Strengths and limitations

This review has sought to provide a comprehensive review of the published prediction models on the effect of various NPIs on SARS-CoV-2 pandemic in India. To the best of the authors' knowledge, this is the first such review undertaken for systematically synthesizing studies that have employed in silico models for such prediction. In this endeavor, preprint articles were included as well, for having a holistic view on the different prediction modeling techniques related to COVID-19. Inclusion of so many preprint articles might be considered a potential source of bias in this review; however, it is believed that since the bulk of studies on this disease and the associated situation is unfolding at a stellar pace, inclusion of preprints adds to a strength in the current evidence generation process altogether. The article not only provides an in-depth understanding of the computational processes in measuring effectiveness of preventive strategies against COVID-19, but it also points out the probable areas in prediction modeling research for the advancement of scientific rigor. Synthesizing the results of these studies helps in providing the policy-makers with precise actionable evidence.

The reviewed articles, however, were neither ranked as per quality of evidence nor bias assessed; however, to ensure proper interpretation of the data, validity statements of the models were presented. It is possible that some relevant papers might have been missed through the systematic literature search strategy, despite best attempts. Another limitation is that articles were considered only in English language. Nonetheless, it is believed that although a more extensive search might have identified additional examples and applications particularly in other languages, it is unlikely to provide new predictive modeling approach, especially in the Indian context.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.


1WHO. Coronavirus Disease (COVID-19)-Events as they Happen. Available from: vel-coronavirus-2019/events-as-they-hap pen. [Last accessed on 2020 Apr 28].
2WHO. Coronavirus. Available from: lth-topics/coron avirus. [Last accessed on 2020 Apr 28].
3Press Information Bureau, Government of India, 2020. PM Calls for Complete Lockdown of Entire Nation for 21 Days. Available from: [Last accessed on 2020 Apr 29].
4Levin SA, Grenfell B, Hastings A, Perelson AS. Mathematical and computational challenges in population biology and ecosystems science. Science 1997;275:334-43.
5Orbann C, Sattenspiel L, Miller E, Dimka J. Defining epidemics in computer simulation models: How do definitions influence conclusions? Epidemics 2017;19:24-32.
6Willem L, Verelst F, Bilcke J, Hens N, Beutels P. Lessons from a decade of individual-based models for infectious disease transmission: A systematic review (2006-2015). BMC Infect Dis 2017;17:612.
7Neyman J. Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability. California (US): University of California Press; 1955.
8Benedictow OJ. Epidemiology of plague: Problems with the use of mathematical epidemiological models in plague research and the question of transmission by human fleas and lice. Can J Infect Dis Med Microbiol 2019;2019:e1542024.
9Smith PG, Morrow RH, Ross DA. Types of Intervention and Their Development. Oxford (UK): OUP Oxford; 2015. Available from: [Last accessed on 2020 May 07].
10Britton T, House T, Lloyd AL, Mollison D, Riley S, Trapman P. Five challenges for stochastic epidemic models involving global transmission. Epidemics 2015;10:54-7.
11Grimm V, Augusiak J, Focks A, Frank BM, Gabsi F, Johnston AS, et al. Towards better modelling and decision support: Documenting model development, testing, and analysis using TRACE. Ecol Model 2014;280:129-39.
12Moons KG, de Groot JA, Bouwmeester W, Vergouwe Y, Mallett S, Altman DG, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: The CHARMS checklist. PLoS Med 2014;11: E1001744.
13Moher D, Liberati A, Tetzlaff J, Altman DG, Group TP. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLOS Med 2009;6:e1000097.
14Singh R, Adhikari R. Age-Structured Impact of Social Distancing on the COVID-19 Epidemic in India. ArXiv200312055 Cond-Mat Q-Bio [Preprint] 2020. Available from: [Last accessed on 2020 Apr 28].
15Chatterjee K, Chatterjee K, Kumar A, Shankar S. Healthcare impact of COVID-19 epidemic in India: A stochastic mathematical model. Medical Journal Armed Forces India. 2020 Apr 2. Available from: [Epub ahead of print]. [Last accessed on 2020 Apr 28].
16Mandal S, Bhatnagar T, Arinaminpathy N, Agarwal A, Chowdhury A, Murhekar M, et al. Prudent public health intervention strategies to control the coronavirus disease 2019 transmission in India: A mathematical model based approach. Indian J Med Res 2020;151:190 9.
17Venkateswaran J, Damani O. Effectiveness of Testing, Tracing, Social Distancing and Hygiene in Tackling Covid-19 in India: A System Dynamics Model. arXiv [Preprint] 2020. Available from: https://arxiv. org/pdf/2004.08859. [Last accessed on 2020 Apr 28].
18Senapati A, Rana S, Das T, Chattopadhyay J. Impact of intervention on the spread of COVID-19 in India: A model based study. arXiv [Preprint] 2020. Available from: [Last accessed on 2020 Apr 28].
19Ghosh P, Basheer S, Paul S, Chakrabarti P, Sarkar J. 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. MedRxiv [Preprint] 2020. Available from: medrxiv/early/2020/04/07/2020.04.05.20054775.full.pdf. [Last accessed on 2020 Apr 28].
20Tomar A, Gupta N. Prediction for the spread of COVID 19 in India and effectiveness of preventive measures. Sci Total Environ 2020;728:138762.
21Kishore R, Jha PK, Das S, Agarwal D, Maloo T, Pegu H, et al. A kinetic model for qualitative understanding and analysis of the effect of complete lockdown imposed by India for controlling the COVID-19 disease spread by the SARS-CoV-2 virus. arXiv [Preprint] 2020. Available from: [Last accessed on 2020 Apr 28].
22Bulchandani VB, Shivam S, Moudgalya S, Sondhi SL. Digital herd immunity and COVID-19. arXiv [Preprint] 2020. Available from: [Last accessed on 2020 Apr 28].
23Ranjan R. Predictions for COVID-19 outbreak in India using Epidemiological models. MedRxiv [Preprint] 2020. Available from: early/2020/04/06/2020.04.02.20051466.full.pdf. [Last accessed on 2020 Apr 28].
24Ghosal S, Sengupta S, Majumder M, Sinha B. Linear regression analysis to predict the number of deaths in India due to SARS CoV 2 at 6 weeks from day 0 (100 cases March 14th 2020). Diabetes Metab Syndr 2020;14:311 5.
25Mandal M, Mandal S. COVID-19 pandemic scenario in India compared to China and rest of the world: A data driven and model analysis. MedRxiv [Preprint] 2020. Available from: content/medrxiv/early/2020/04/24/2020.04.20.20072744.full.pdf. [Last accessed on 2020 Apr 28].
26Mishra PK, Mishra S. A deductive approach to modeling the spread of COVID-19. MedRxiv [Preprint] 2020. Available from: https://www. full.pdf. [Last accessed on 2020 Apr 28].
27Ray D, Salvatore M, Bhattacharyya R, Wang L, Mohammed S, Purkayastha S, et al. Predictions, role of interventions and effects of a historic national lockdown in India's response to the COVID-19 pandemic: data science call to arms. MedRxiv [Preprint] 2020. Available from: early/2020/04/18/2020.04.15.20067256.full.pdf. [Last accessed on 2020 Apr 28].
28Mazumder A, Arora M, Bharadiya V, Berry P, Agarwal M, Gupta M, et al. Study of Epidemiological Characteristics and In-silico Analysis of the Effect of Interventions in the SARS-CoV-2 Epidemic in India. MedRxiv [Preprint] 2020. Available from: content/medrxiv/early/2020/04/07/2020.04.05.20053884.full.pdf. [Last accessed on 2020 Apr 28].
29Shah PV. Prediction of the Peak, Effect of Intervention and Total Infected by the Coronavirus Disease in India. MedRxiv [Preprint] 2020. Available from: early/2020/04/24/2020.04.20.20072793.full.pdf. [Last accessed on 2020 Apr 28].
30Jindal VK. COVID 19, a realistic model for saturation, growth and decay of the India specific disease. arXiv [Preprint] 2020. Available from: [Last accessed on 2020 Apr 28].
31Das S, Ghosh P, Sen B, Mukhopadhyay I. Critical community size for COVID-19--a model based approach to provide a rationale behind the lockdown. arXiv [Preprint] 2020. Available from: pdf/2004.03126. [Last accessed on 2020 Apr 28].
32Sardar T, Nadim SS, Chattopadhyay J. Assessment of 21 days lockdown effect in some states and overall India: a predictive mathematical study on COVID-19 outbreak. arXiv [Preprint] 2020. Available from: https:// [Last accessed on 2020 Apr 28].
33Dhanwant JN, Ramanathan V. Forecasting COVID 19 growth in India using Susceptible-Infected-Recovered (SIR) model. arXiv [Preprint] 2020. Available from: [Last accessed on 2020 Apr 28].
34Paul A, Chatterjee S, Bairagi N. Prediction on Covid-19 epidemic for different countries: Focusing on South Asia under various precautionary measures. MedRxiv [Preprint] 2020. Available from: https://www. full.pdf. [Last accessed on 2020 Apr 28].
35Pant R, Choudhry LP, Rajesh JG, Yeldandi VV. COVID-19 Epidemic Dynamics and Population Projections from Early Days of Case Reporting in a 40 million population from Southern India. MedRxiv [Preprint] 2020. Available from: medrxiv/early/2020/04/21/2020.04.17.20070292.full.pdf. [Last accessed on 2020 Apr 28].
36Mukhopadhyay S, Chakraborty D. Estimation of undetected COVID-19 infections in India. MedRxiv [Preprint] 2020. Available from: https://www. full.pdf. [Last accessed on 2020 Apr 28].
37Bhola J, Venkateswaran VR, Koul M. Corona Epidemic in Indian context: Predictive Mathematical Modelling. MedRxiv [Preprint] 2020. Available from: early/2020/04/07/2020.04.03.20047175.full.pdf. [Last accessed on 2020 Apr 28].
38Nussbaumer-Streit B, Mayr V, Dobrescu AI, Chapman A, Persad E, Klerings I, et al. Quarantine alone or in combination with other public health measures to control COVID-19: A rapid review. Cochrane Database Syst Rev 2020;4:CD013574.
39Viner RM, Russell SJ, Croker H, Packer J, Ward J, Stansfield C, et al. School closure and management practices during coronavirus outbreaks including COVID-19: A rapid systematic review. Lancet Child Adolesc Health 2020;4:397-404.
40Shah K, Awasthi A, Modi B, Kundapur R, Saxena D. Unfolding trends of COVID-19 transmission in India: Critical review of available Mathematical models. Indian J Community Health 2020; 32:206-14.