Open Research and Contributor Identifier

ORCID - 0000-0001-6393-1943

Department

Epidemiology and Biostatistics

Education

• University of Trento, Italy, Ph.D., 2016

• Tomsk Polytechnic University, Russia, M.A., 2010

• Tomsk Polytechnic University, Russia, B.A., 2008

Background

• 2019-2020 Postdoctoral Associate, ISI Foundation, Turin, Italy
• 2017-2019 Postdoctoral Associate, Network Science Institute, Northeastern University, Boston, MA, USA

Scholarly Interest

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:

• Human contact networks and infectious disease modeling. Dr. Litvinova have incorporated socio-economic micro and macro data into infectious disease modeling for multiple diseases and countries. She has created contact matrices and detailed synthetic populations that describe who meets whom, and international mobility networks that account for the characteristics of travelers. These contributions have since been incorporated into computational models to forecast COVID-19 transmission, to understand its spatiotemporal dispersion in the US and Europe, to understand the most probable within-city locations of the spread of respiratory infectious disease (COVID-19 and influenza) and model targeted interventions.
• COVID-19 epidemiology and SARS-CoV-2 transmission. 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.
• NPIs to control the epidemic spread. Dr. Litvinova is particularly interested in modeling and estimating the effects of such non-pharmaceutical interventions (NPIs) as reactive school/class closures, travel restrictions, regular screening, testing, tracing, and household quarantine and isolation. Her findings have quantified the effectiveness of the reactive school and class closures for counteracting seasonal and pandemic influenza. Drawing on the developed contact patterns data, surveys, synthetic populations, meta-population, and agent-based modeling, her research has also increased our understanding of the impact of these measures on the epidemic dynamics of COVID-19.
• Vulnerability and social networks. COVID-19 has been the perfect example of how population behavior and socio-economic disparities significantly affect the impact of interventions. Dr. Litvinova have previously illustrated that not only the disease burden is heterogenous, but also group-specific transmission risks. The very nature of the infection transmission process makes the social networks the main pathway for transmission, thus resulting in clusters of infections. Dr Litvinova is interested in introducing elements of social network analysis to the investigation of infectious disease dynamics to understand the association between vulnerability to infection and heterogeneity in behavior and social networks. She currently works on merging social network analysis with the individual-based infectious disease modeling to estimate risks of infection and disease burden for the elderly and other vulnerable individuals and finding better ways to reduce these risks.
• Arboviral diseases. Globalization and urbanization are among the main protagonists responsible for the recent dramatic change in the epidemiology of arboviral (arthropod-borne) diseases. In recent years, we are witnessing the diffusion of invasive species in new areas of the world and in human-built environments that, coupled with the unprecedented level of cross-species interactions and continuously growing human mobility, is sparking the outbreaks of mosquito-borne diseases in temperate countries. Research agenda of Dr. Litvinova includes supporting the fight against arboviral diseases through the development of data-driven multi-scale transmission models that merge the dynamics of hosts, vectors and the socio-economic characteristics regulating human exposure to mosquito vectors.
• 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

• Google profile: Click here
• NCBI Bibliography: Click here

Selected Publications

Articles

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.

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.

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.

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, 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.

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.

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.