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Assistant Professor
Open Research and Contributor Identifier |
DepartmentEpidemiology and Biostatistics |
Education
- B.A. Tomsk Polytechnic University, Russia 2008
- M.A. Tomsk Polytechnic University, Russia 2010
- Ph.D. University of Trento, Italy 2016
Background
- 2023 – present Assistant Professor, Department of Epidemiology and Biostatistics, School of Public Health, Indiana University, Bloomington, IN, USA
- 2022 – present Affiliate Faculty, Robert F. Byrnes Russian and East European Institute, Hamilton Lugar School of Global and International Studies, Indiana University, Bloomington, IN, USA
- 2022-2023 Assistant Research Scientist, Department of Epidemiology and Biostatistics, School of Public Health, Indiana University, Bloomington, IN, USA
- 2020-2022 Postdoctoral Fellow, Department of Epidemiology and Biostatistics, School of Public Health, Indiana University, Bloomington, IN, USA
- 2019-2020 Postdoctoral Associate, ISI Foundation, Turin, Italy
- 2017-2019 Postdoctoral Associate, Network Science Institute, Northeastern University, Boston, MA, USA
Research Interests
Dr. Litvinova's research leverages data analysis, statistical and mathematical modeling to investigate the determinants of infectious disease epidemiology and to evaluate potential prevention, mitigation, and control policies. One of her interests lies in understanding of the connections between social, economic, and demographic contexts and the spread of infectious diseases. The main goal of her research is to provide actionable insights for the support of public health decision-making. Dr. Litvinova continues to be interested in the research in the following areas:
Infectious disease transmission, social and contact networks.
In particular, Dr. Litvinova's research focuses on understanding human contact networks, their connection with the social networks and social interactions between individuals in different contexts and incorporating this understanding into computational modeling of infectious diseases. Dr. Litvinova have analyzed socio-economic micro and macro data and created the quantitative descriptions of human interactions in the forms of contact matrices for meta-population computational models and detailed synthetic populations for agent-based simulations. She also advanced understanding of the characteristics of passengers within different international mobility networks. These contributions have since been used to forecast COVID-19 transmission, understand its spatiotemporal dispersion in the US and Europe, understand the most probable within-city locations of the spread of respiratory infectious disease (COVID-19 and influenza) and model targeted interventions. She is currently focusing on integrating different streams of data on human interactions to better understand and predict contact networks and create more reliable computational models of infectious disease transmission.
Epidemiologic characteristics of infectious diseases and NPIs to control the epidemic spread.
The COVID-19 pandemic has been and continues to be an unprecedented challenge for the world. To control the spread of a novel disease, it is necessary to understand its epidemiologic characteristics. Dr. Litvinova's research has contributed to the estimation of key features of SARS-CoV-2 transmission patterns (e.g., infectiousness, transmission risks), key time-to-event parameters (incubation period, generation time, serial interval), and characteristics of cases. These estimations have been widely used in research studies worldwide. Before COVID-19, she estimated epidemiologic characteristics of Ebola and influenza. Dr. Litvinova is particularly interested in integrating estimated epidemiologic characteristics into modeling of transmission process to evaluate the effects of non-pharmaceutical interventions (NPIs). By drawing on the developed contact patterns data, surveys, synthetic populations, meta-population, and agent-based modeling, she has performed scenario analysis to estimate the effect of such NPIs as reactive school/class closures, travel restrictions, regular screening, testing, tracing, household quarantine and isolation for counteracting COVID-19, seasonal and pandemic influenza.
Health disparities, socio-economic drivers of transmission and vulnerable populations.
COVID-19 serves as a compelling example of how both population behavior and socio-economic disparities play a critical role in shaping the burden of infectious diseases and the effectiveness of interventions. Dr. Litvinova's research has shed light on the fact that disease burden is not uniform, and specific groups face distinct risks of transmission. Residential and occupational segregation are key factors that influence who interact with whom, where these interactions occur, and to what extent during outbreaks. These factors consequently define the exposure to airborne pathogens. Additionally, Dr. Litvinova's research suggests that occupational disparities contribute to the vulnerability of certain populations to arboviral diseases. Those already experiencing health disparities are likely to be disproportionately affected by such outbreaks.
Her research agenda focuses on understanding the intricate connections between different demographic, socio-economic and ecologic processes and unevenly distributed exposure to arboviral and respiratory diseases. To achieve this, she develops data-driven multi-scale transmission models that incorporate the dynamics of population interactions with both humans and mosquito vectors. Moreover, she examines how socio-economic characteristics influence disease-specific exposure among human populations. By investigating these complex interactions, Dr. Litvinova's work aims to identify strategies for reducing health disparities and developing targeted interventions to protect vulnerable populations from infectious diseases. Through her data-driven approach, she seeks to contribute to more effective public health policies that address the underlying socio-economic drivers of transmission and promote health equity.
Economics and epidemics.
The effect of the pandemic on global value chains, labor market, and economic stability is undeniable, but is it permanent? The pandemic highlighted the weaknesses of our society both locally and globally, but the attempts to forecast and understand the effect of the pandemic on economy are highly fragmented. Epidemiologists measure the disease burden in terms of pressure on the healthcare system, infections, and deaths, while economists look at production, consumption, and income changes due to control strategies. As a result, policy makers are often forced to choose between health and economy, although they are intrinsically interconnected. Currently, Dr. Litvinova plans to utilize agent-based models to merge infectious disease modelling with economic modelling to incorporate changes in production, labor force and consumption to evaluate the short-term and long-term effects of each public health policy both on the health and economy of the society.
Full list of publications
NCBI Bibliography: NCBI Bibliography
Selected Publications
Articles
Litvinova M, Liu QH, Kulikov ES, Ajelli M. Reactive school closure weakens the network of social interactions and reduces the spread of influenza.
Proc Natl Acad Sci U S A. 2019 Jul 2;116(27):13174-13181. doi: 10.1073/pnas.1821298116. Epub 2019 Jun 17. PubMed PMID: 31209042; PubMed Central PMCID: PMC6613079.
Sun K, Wang W, Gao L, Wang Y, Luo K, Ren L, Zhan Z, Chen X, Zhao S, Huang Y, Sun Q, Liu Z, Litvinova M, Vespignani A, Ajelli M, Viboud C, Yu H.
Transmission heterogeneities, kinetics, and controllability of SARS-CoV-2. Science. 2021 Jan 15;371(6526). doi: 10.1126/science.abe2424.
Epub 2020 Nov 24. PubMed PMID: 33234698; PubMed Central PMCID: PMC7857413.
Zhang J, Litvinova M, Wang W, Wang Y, Deng X, Chen X, Li M, Zheng W, Yi L, Chen X, Wu Q, Liang Y, Wang X, Yang J, Sun K, Longini IM Jr, Halloran ME, Wu P, Cowling BJ, Merler S, Viboud C, Vespignani A,
Ajelli M, Yu H. Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study.
Lancet Infect Dis. 2020 Jul;20(7):793-802. doi: 10.1016/S1473-3099(20)30230-9. Epub 2020 Apr 2. PubMed PMID: 32247326; PubMed Central PMCID: PMC7269887.
Liu QH, Zhang J, Peng C, Litvinova M, Huang S, Poletti P, Trentini F, Guzzetta G, Marziano V, Zhou T, Viboud C, Bento AI, Lv J, Vespignani A, Merler S, Yu H, Ajelli M. Model-based evaluation of alternative reactive class closure strategies against COVID-19.
medRxiv. 2021 Apr 23;. doi: 10.1101/2021.04.18.21255683. PubMed PMID: 33907769; PubMed Central PMCID: PMC8077629.
Zhang J, Litvinova M, Liang Y, Wang Y, Wang W, Zhao S, Wu Q, Merler S, Viboud C, Vespignani A, Ajelli M, Yu H. Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China. Science. 2020 Jun 26;368(6498):1481-1486.
PubMed Central PMCID: PMC7199529.
Mistry D, Litvinova M, Pastore Y Piontti A, Chinazzi M, Fumanelli L, Gomes MFC, Haque SA, Liu QH, Mu K, Xiong X, Halloran ME, Longini IM Jr, Merler S, Ajelli M, Vespignani A.
Inferring high-resolution human mixing patterns for disease modeling. Nat Commun. 2021 Jan 12;12(1):323. PubMed Central PMCID: PMC7803761.
Davis JT, Chinazzi M, Perra N, Mu K, Pastore Y Piontti A, Ajelli M, Dean NE, Gioannini C, Litvinova M, Merler S, Rossi L, Sun K, Xiong X, Longini IM Jr, Halloran ME, Viboud C, Vespignani A.
Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave. Nature. 2021 Dec;600(7887):127-132. PubMed Central PMCID: PMC8636257.