HOW IT WORKS

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A. Mathematical models can predict how infectious diseases like COVID-19 will spread under certain conditions. We developed a model for COVID-19 transmission that measures how red-light areas can contribute to the growth of the epidemic in India. This enables us to see what will likely happen if red-light areas are reopened or if they are kept closed.

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B. Our model takes into account all of the potential outcomes resulting from an infection. Anybody can get infected. When someone is exposed to the virus, they can either have no symptoms, mild symptoms, or severe symptoms. People with no symptoms or mild symptoms will recover on their own. Individuals with severe symptoms will either be hospitalized or sent to the ICU (intensive care unit). Those people will then either recover or die.

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C. The spread of infection from person to person within a population depends on the prevalence of infection at the given time, the patterns of person to person contact (specific to each age group), and the rate of viral transmission for each instance of contact.

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D. We separated the general population from the red-light residents, which includes sex workers as well as brothel managers, security, support staff, etc. Then both populations were divided into 4 age groups. The general population and RLA population were further segmented based on the known history of COVID-19. Each subpopulation has its own dynamic of prevalence, contact pattern, and transmissibility. We ran the model against these subpopulations in order to see the difference in how they affect the spread of the virus.

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E. Red-light areas in India are densely populated and have a high number of interactions when they are open. Their contact pattern is high. The intimacy of the act of sex also ensures a higher than normal transmission rate for each interaction. Those factors are compounded with the sheer volume of unique customers that enter these areas on a daily basis and then return to their home neighborhoods (5 lakhs, nationwide). Thus, the model shows a massive increase in cases and deaths for every population when red-light areas are open versus when they are closed.

AUTHORS OF THE STUDY

Alison P. Galvani
Alison P. Galvani
Center for Infectious Disease Modeling & Analysis, Yale University, New Haven, CT

Alison P. Galvani is one of the top disease modeling experts in the world. She is the Director of the Center for Infectious Disease Modeling and Analysis (CIDMA) and the Burnett and Stender Families Professor of Epidemiology (Microbial Diseases) at the Yale School of Public Health . Professor Galvani's research focuses on integrating epidemiology and evolutionary ecology or economics in order to generate predictions that could not be made by these disciplines alone. This interdisciplinary approach has widespread potential for answering evolutionary questions, explaining empirical observations, and informing public health policy. Professor Galvani has applied modeling and cost-effectiveness analyses to improve national and international public health policies of influenza, TB and HPV, HIV, rotavirus and Ebola, among other diseases. She has published over 160 scholarly articles. She is the youngest faculty member in Yale School of Medicine's history to be appointed to a named professorship.

Link to Publications:
https://medicine.yale.edu/profile/alison_galvani/?tab=research

Sudhakar V. Nuti
Sudhakar V. Nuti
Harvard Medical School, Boston, MA.

Department of Medicine, Massachusetts General Hospital, Boston, MA. A Forbes 30 Under 30 in Healthcare, Nuti studies variations in the quality of healthcare in the United States, an effort that has resulted in multiple publications in the Journal of the American Medical Association. One recent finding: despite the controversy over the quality of care provided by the U.S.Department of Veterans Affairs, there was no measurable difference in quality between the VA and other hospitals.

Link to Publications:
https://connects.catalyst.harvard.edu/Profiles/display/Person/180012

Abhishek Pandey
Abhishek Pandey
Center for Infectious Disease Modeling & Analysis, Yale University, New Haven, CT

Dr. Pandey is the Associate Director of the Yale Center for Infectious Disease Modeling and Analysis at the Yale School of Public Health. In his research, Dr. Pandey develops data-driven mathematical models of infectious diseases to improve our understanding of the transmission dynamics, evaluate tools and strategies for disease control, inform public health policies, and provide tools for public health decision making. His current research focuses on understanding the impact of socio-political conflicts on public health response amidst disease outbreaks and evaluating control interventions for HIV and neglected tropical diseases such as sleeping-sickness. He graduated with a BSc (Hons) degree in Mathematics from Ramjas College, Delhi University and a MSc degree in Applied Mathematics from Indian Institute of Technology Roorkee. He obtained his PhD in Mathematics at Clemson University.

Link to Publications:
https://medicine.yale.edu/profile/abhishek_pandey/?tab=research

Jeffrey P. Townsend
Jeffrey P. Townsend
Department of Biostatistics, Yale School of Public Health, New Haven, CT.

Professor Townsend is the Yale Elihu Professor of Biostatistics and Ecology & Evolutionary Biology. He has done research on diseases across the world including in India as well as has conducted impactful research on Ebola during the outbreak in Liberia. He received his Ph.D. in 2002 in organismic and evolutionary biology from Harvard University, under the advisement of Daniel Hartl. His Ph.D. was entitled "Population genetic variation in genome-wide gene expression: modeling, measurement, and analysis", and constituted the first population genetic analysis of genome-wide gene expression variation. After making use of the model budding yeast S. cerevisiae for his Ph.D. research, Dr. Townsend accepted an appointment as a Miller Fellow at the University of California-Berkeley in the Department of Plant and Microbial Biology, where he worked to develop molecular tools, techniques, and analysis methodologies for functional genomics studies with the filamentous fungal model species Neurospora crassa, co-advised by Berkeley fungal evolutionary biologist John Taylor and molecular mycologist Louise Glass. In 2004, he accepted his first appointment as an Assistant Professor in the Department of Molecular and Cell Biology at the University of Connecticut. In 2006 he was appointed as an Assistant Professor the Department of Ecology and Evolutionary Biology at Yale University. He is currently serving as the Yale Elihu Professor of Biostatistics and Ecology & Evolutionary Biology.

Link to Publications:
https://medicine.yale.edu/profile/jeffrey_townsend/?tab=research

Pratha Sah
Pratha Sah
Center for Infectious Disease Modeling & Analysis, Yale University, New Haven, CT

Pratha is a postdoctoral associate at the Center for Infectious Disease Modeling and Analysis. She completed her PhD in epidemiology and infectious disease modeling at Georgetown University, Washington DC. Her research is focused on applications of quantitative methods in the study of infectious disease outbreaks. She uses mathematical, statistical and network models to understand how policy, human behavior and the environment influences the disease transmission; and how disease outbreaks, in turn, shape local and global policy.

Link to Publications:
https://medicine.yale.edu/profile/pratha_sah/