Multi-city modeling of epidemics using spatial networks: Application to 2019-nCov (COVID-19) coronavirus in India

Pujari, Bhalchandra S.; Shekatkar, Snehal M.

doi:10.1101/2020.03.13.20035386

Cited by 49 publications

(44 citation statements)

References 12 publications

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“…Hill et al (2010) also showed decisively that social networks matter very much to the way that a virus is passed on. Pujari and Shekatkar (2020) analysed social network patterns in India. Verity et al (2020:3) said that the cruise ship 'Diamond Princess' data gave the best insight to the biological processes of exposure and infection.…”

Section: Models Of Transmission and Severity Of Sars-cov-19mentioning

confidence: 99%

Modelling COVID-19 With More Disaggregation and Less Nomothetic Parameterisation: UK and India Examples

Olsen

Bera

Kim

et al. 2020

Preprint

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Modelling of pandemic vulnerability in a development context can be improved through combining disciplines, combining data, and recognising the many nested levels of the epidemic. Models of transmission have been constructed at national level or for multiple nations. We instead construct a model allowing for social-group differentials in risk, along with conditioning regional factors and lifestyle factors. Severe COVID-19 disease is our innovative key outcome. We use three data sources at once: National Family and Health Survey for India, Indian Census 2011, and COVID-19 deaths. We provide results for 11 states of India, enabling best-yet targeting of policy actions. The future uses of such models are many. COVID-19 deaths in north and central India were higher in areas with older populations and overweight populations, and was more common among those with pre-existing health conditions, or who smoke or live in urban areas. Policy experts may both want to ‘follow World Health Organisation advice’ and yet also use disaggregated and spatially-specific data to improve wellbeing outcomes during the pandemic.

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Section: Models Of Transmission and Severity Of Sars-cov-19mentioning

confidence: 99%

Modelling COVID-19 With More Disaggregation and Less Nomothetic Parameterisation: UK and India Examples

Olsen

Bera

Kim

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Pujari et al attempted multicity model and assumed the fraction of population reaching a neighbor is inversely proportional to its degree connections. [15] COVID-19 currently affects almost all developed and developing countries; India is one of the countries with significant COVID-19 cases. India is currently in stage 2 of epidemics, and strict plans and steps are required to prevent it from entering stage 3 or higher.…”

Section: Introductionmentioning

confidence: 99%

Modeling geographical spread of COVID-19 in India using network-based approach

Kumar¹

2020

Preprint

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COVID-19 pandemic is a global concern, due to its high spreading and alarming fatality rate. Mathematical models can play a decisive role in mitigating the spread and predicting the growth of the epidemic. India is a large country, with a highly variable inter-state mobility, and dynamically varying infection cases in different locations; thus, the existing models, based solely on the aspects of growth rates, or generalized network concepts, may not provide desired predictions. The internal mobility of a country must be considered, for accurate prediction. Herein, we propose a framework for predicting the geographical spread of COVID-19, using reported COVID-19 cases, census migration data, and monthly airline data of passengers. We discover that spreading depends on the spatial distribution of existing cases, human mobility patterns, and administrative decisions. In India, the mobility towards professional sites can surge incoming cases at Maharastra and Karnataka, while migration towards the native places can risk Uttar Pradesh and Bihar. We anticipate that the state Kerala, with one of the highest cases of COVID-19, may not receive significant incoming cases, while Karnataka and Haryana may receive the challenge of high incoming cases, with medium cases so far. Using airline passenger's data, we also estimate the number of potential incoming cases at various airports. The study predicts that the airports located in the region of north India are vulnerable, whereas in northeast India and in some south India are relatively safe. The detailed analysis in this direction will guide policymakers for prior planning of transport, and minimize the spread of COVID-19.

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“…21,22 . Additionally, various factors regulating disease transmission have also been considered such as the effect of lockdowns and movement or migration patterns between cities by using the transport networks that contribute heavily towards the spread of the disease 23 . Over the years, although these kinds of SEIR models have been invaluable in the study of epidemiology and are intrinsically capable of predicting disease dynamics with precision, their accuracy, however, is heavily dependent on the correct estimations of the several unknown parameters that regulate the model outcome.…”

Section: Introductionmentioning

confidence: 99%

On the Temporal Analysis of COVID-19 Pandemic and Prediction of R <sub>0</sub>

et al. 2020

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The COVID-19 pandemic has affected millions of people and claimed numerous lives already. As of now, there are no available treatments and it has become both imperative and challenging to forecast the COVID-19 cases, which will help to design effective clinical management and policy to fight the pandemic. With the objective to forecast the COVID-19 cases and Basic Reproductive Number (R0) country-wise, for more than a month ahead, we have adopted a data driven approach that employs Multiple Aggregation Prediction Algorithm (MAPA) for temporal predictions. Our strategy applies MAPA in two ways. The first is the direct application on the number of cases and second is by calculation of R0, followed by MAPA. This novel workflow generates a Principal and an Exponential Prediction that provides a range of values within which the total number of cases is expected to lie. The strategy and workflow have been validated for long term predictions with 51 countries in different growth phases. Thereafter, we have made predictions of the possible number of COVID-19 cases for the next 45 days of these 51 countries, the world as a whole and the other 160 countries combined, that are affected by the pandemic.

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Multi-city modeling of epidemics using spatial networks: Application to 2019-nCov (COVID-19) coronavirus in India

Cited by 49 publications

References 12 publications

Modelling COVID-19 With More Disaggregation and Less Nomothetic Parameterisation: UK and India Examples

Modelling COVID-19 With More Disaggregation and Less Nomothetic Parameterisation: UK and India Examples

Modeling geographical spread of COVID-19 in India using network-based approach

On the Temporal Analysis of COVID-19 Pandemic and Prediction of R <sub>0</sub>

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