Prediction and forecast for COVID-19 Outbreak in India based on Enhanced Epidemiological Models

Epidemiological Modeling supports the evaluation of various disease management activities. The value of epidemiological models lies in their ability to study various scenarios and to provide governments with a priori knowledge of the consequence of disease incursions and the impact of preventive strategies. A prevalent method of modeling the spread of pandemics is to categorize individuals in the population as belonging to one of several distinct compartments, which represents their health status with regard to the pandemic. In this work, a modified SIR epidemic model is proposed and analyzed with respect to the identification of its parameters and initial values based on stated or recorded case data from public health sources to estimate the unreported cases and the effectiveness of public health policies such as social distancing in slowing the spread of the epidemic. The analysis aims to highlight the importance of unreported cases for correcting the underestimated basic reproduction number. In many epidemic outbreaks, the number of reported infections is likely much lower than the actual number of infections which can be calculated from the model’s parameters derived from reported case data. The analysis is applied to the COVID-19 pandemic for several countries in the Gulf region and Europe.

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Understanding mosquito host-choice behaviour: a new and low-cost method of identifying the sex of human hosts from mosquito blood meals

Parasites & Vectors ◽

10.1186/s13071-021-04577-w ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Fiona Teltscher ◽

Sophie Bouvaine ◽

Gabriella Gibson ◽

Paul Dyer ◽

Jennifer Guest ◽

...

Keyword(s):

Blood Meal ◽

Host Species ◽

Disease Transmission ◽

Low Cost ◽

Host Choice ◽

Epidemiological Models ◽

Choice Behaviour ◽

Vector Borne ◽

Transmission Models ◽

Blood Meals

Abstract Background Mosquito-borne diseases are a global health problem, causing hundreds of thousands of deaths per year. Pathogens are transmitted by mosquitoes feeding on the blood of an infected host and then feeding on a new host. Monitoring mosquito host-choice behaviour can help in many aspects of vector-borne disease control. Currently, it is possible to determine the host species and an individual human host from the blood meal of a mosquito by using genotyping to match the blood profile of local inhabitants. Epidemiological models generally assume that mosquito biting behaviour is random; however, numerous studies have shown that certain characteristics, e.g. genetic makeup and skin microbiota, make some individuals more attractive to mosquitoes than others. Analysing blood meals and illuminating host-choice behaviour will help re-evaluate and optimise disease transmission models. Methods We describe a new blood meal assay that identifies the sex of the person that a mosquito has bitten. The amelogenin locus (AMEL), a sex marker located on both X and Y chromosomes, was amplified by polymerase chain reaction in DNA extracted from blood-fed Aedes aegypti and Anopheles coluzzii. Results AMEL could be successfully amplified up to 24 h after a blood meal in 100% of An. coluzzii and 96.6% of Ae. aegypti, revealing the sex of humans that were fed on by individual mosquitoes. Conclusions The method described here, developed using mosquitoes fed on volunteers, can be applied to field-caught mosquitoes to determine the host species and the biological sex of human hosts on which they have blood fed. Two important vector species were tested successfully in our laboratory experiments, demonstrating the potential of this technique to improve epidemiological models of vector-borne diseases. This viable and low-cost approach has the capacity to improve our understanding of vector-borne disease transmission, specifically gender differences in exposure and attractiveness to mosquitoes. The data gathered from field studies using our method can be used to shape new transmission models and aid in the implementation of more effective and targeted vector control strategies by enabling a better understanding of the drivers of vector-host interactions.

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THE VALUE OF ROBUST STATISTICAL FORECASTS IN THE COVID-19 PANDEMIC

National Institute Economic Review ◽

10.1017/nie.2021.9 ◽

2021 ◽

Vol 256 ◽

pp. 19-43

Author(s):

Jennifer L. Castle ◽

Jurgen A. Doornik ◽

David F. Hendry

Keyword(s):

Structural Change ◽

Measurement Errors ◽

Epidemiological Models ◽

Short Term ◽

Data Measurement ◽

Policy Interventions ◽

Changing Trends ◽

Technological Advances ◽

The Difference ◽

The Uk

The Covid-19 pandemic has put forecasting under the spotlight, pitting epidemiological models against extrapolative time-series devices. We have been producing real-time short-term forecasts of confirmed cases and deaths using robust statistical models since 20 March 2020. The forecasts are adaptive to abrupt structural change, a major feature of the pandemic data due to data measurement errors, definitional and testing changes, policy interventions, technological advances and rapidly changing trends. The pandemic has also led to abrupt structural change in macroeconomic outcomes. Using the same methods, we forecast aggregate UK unemployment over the pandemic. The forecasts rapidly adapt to the employment policies implemented when the UK entered the first lockdown. The difference between our statistical and theory based forecasts provides a measure of the effect of furlough policies on stabilising unemployment, establishing useful scenarios had furlough policies not been implemented.

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Coupling Epidemiological Models with Social Dynamics

Bulletin of Mathematical Biology ◽

10.1007/s11538-021-00910-7 ◽

2021 ◽

Vol 83 (7) ◽

Author(s):

Carlo Giambiagi Ferrari ◽

Juan Pablo Pinasco ◽

Nicolas Saintier

Keyword(s):

Social Dynamics ◽

Epidemiological Models

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Incorporating false negative tests in epidemiological models for SARS-CoV-2 transmission and reconciling with seroprevalence estimates

Scientific Reports ◽

10.1038/s41598-021-89127-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rupam Bhattacharyya ◽

Ritoban Kundu ◽

Ritwik Bhaduri ◽

Debashree Ray ◽

Lauren J. Beesley ◽

...

Keyword(s):

False Negative ◽

Population Based ◽

Training Data ◽

Epidemiological Models ◽

Rt Pcr ◽

Antibody Prevalence ◽

Igg Antibody ◽

False Negatives ◽

Seir Model ◽

Study Population

AbstractSusceptible-Exposed-Infected-Removed (SEIR)-type epidemiologic models, modeling unascertained infections latently, can predict unreported cases and deaths assuming perfect testing. We apply a method we developed to account for the high false negative rates of diagnostic RT-PCR tests for detecting an active SARS-CoV-2 infection in a classic SEIR model. The number of unascertained cases and false negatives being unobservable in a real study, population-based serosurveys can help validate model projections. Applying our method to training data from Delhi, India, during March 15–June 30, 2020, we estimate the underreporting factor for cases at 34–53 (deaths: 8–13) on July 10, 2020, largely consistent with the findings of the first round of serosurveys for Delhi (done during June 27–July 10, 2020) with an estimated 22.86% IgG antibody prevalence, yielding estimated underreporting factors of 30–42 for cases. Together, these imply approximately 96–98% cases in Delhi remained unreported (July 10, 2020). Updated calculations using training data during March 15-December 31, 2020 yield estimated underreporting factor for cases at 13–22 (deaths: 3–7) on January 23, 2021, which are again consistent with the latest (fifth) round of serosurveys for Delhi (done during January 15–23, 2021) with an estimated 56.13% IgG antibody prevalence, yielding an estimated range for the underreporting factor for cases at 17–21. Together, these updated estimates imply approximately 92–96% cases in Delhi remained unreported (January 23, 2021). Such model-based estimates, updated with latest data, provide a viable alternative to repeated resource-intensive serosurveys for tracking unreported cases and deaths and gauging the true extent of the pandemic.

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