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1. Spatial scales of COVID-19 transmission in Mexico

   https://doi.org/10.1093/pnasnexus/pgae306  

   Klein, Brennan; Hartle, Harrison; Shrestha, Munik; Zenteno, Ana Cecilia; Barros_Sierra_Cordera, David; Nicolás-Carlock, José R; Bento, Ana I; Althouse, Benjamin M; Gutierrez, Bernardo; Escalera-Zamudio, Marina; et al (September 2024, PNAS Nexus)

   Galea, Sandro (Ed.)

   Abstract During outbreaks of emerging infectious diseases, internationally connected cities often experience large and early outbreaks, while rural regions follow after some delay. This hierarchical structure of disease spread is influenced primarily by the multiscale structure of human mobility. However, during the COVID-19 epidemic, public health responses typically did not take into consideration the explicit spatial structure of human mobility when designing nonpharmaceutical interventions (NPIs). NPIs were applied primarily at national or regional scales. Here, we use weekly anonymized and aggregated human mobility data and spatially highly resolved data on COVID-19 cases at the municipality level in Mexico to investigate how behavioral changes in response to the pandemic have altered the spatial scales of transmission and interventions during its first wave (March–June 2020). We find that the epidemic dynamics in Mexico were initially driven by exports of COVID-19 cases from Mexico State and Mexico City, where early outbreaks occurred. The mobility network shifted after the implementation of interventions in late March 2020, and the mobility network communities became more disjointed while epidemics in these communities became increasingly synchronized. Our results provide dynamic insights into how to use network science and epidemiological modeling to inform the spatial scale at which interventions are most impactful in mitigating the spread of COVID-19 and infectious diseases in general. 

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2. A novel approach to forecasting reproduction numbers of spatiotemporal stochastic epidemic spread using a PDE-based model and real-time infection data

   https://doi.org/10.1038/s41598-025-91811-5  

   David, Deepak Antony; Liu, Chunyan; Street, Logan; Ehrlich, Shelley; Kumar, Manish; Ramakrishnan, Subramanian (December 2025, Scientific Reports)

   The COVID-19 pandemic highlighted the need for improved epidemic spread forecasting, a critical precursor for developing optimal control measures for spread mitigation. Well-recognized shortcomings in computing basic and effective reproduction numbers (R0, Re)-fundamental metrics for forecasting-underscore the need for new methods for estimating them from available data. We present a novel computational framework for estimating reproduction numbers from empirical spread data. The framework is derived from a mechanistic, spatiotemporal, Partial Differential Equation (PDE) model of epidemic spread utilizing mathematical results from PDE epidemic models. Forecasts of spatiotemporal effective reproduction number Re using the framework are found to be in excellent agreement with COVID-19 spread trends for Hamilton County, Ohio, USA, for three distinct periods. Furthermore, the forecasts are shown to align with corresponding reproduction numbers computed independently using the Wallinga-Teunis and Cori retrospective methods used in epidemiology. In summary, the results establish the validity of the framework and indicate applicability to future epidemics- especially for regions such as counties and for timeframes extending in weeks -even during dynamic phases when obtainable real-time infection spread data will likely be sparse. 

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3. Network Modeling and Analysis of COVID-19 Testing Strategies

   https://doi.org/10.1109/EMBC46164.2021.9629754  

   Zhang, Siqi; Ventura, Marta J.; Yang, Hui (November 2021, 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC))

   The COVID-19 preparedness plans by the Centers for Disease Control and Prevention strongly underscores the need for efficient and effective testing strategies. This, in turn, calls upon the design and development of statistical sampling and testing of COVID-19 strategies. However, the evaluation of operational details requires a detailed representation of human behaviors in epidemic simulation models. Traditional epidemic simulations are mainly based upon system dynamic models, which use differential equations to study macro-level and aggregated behaviors of population subgroups. As such, individual behaviors (e.g., personal protection, commute conditions, social patterns) can’t be adequately modeled and tracked for the evaluation of health policies and action strategies. Therefore, this paper presents a network-based simulation model to optimize COVID-19 testing strategies for effective identifications of virus carriers in a spatial area. Specifically, we design a data-driven risk scoring system for statistical sampling and testing of COVID-19. This system collects real-time data from simulated networked behaviors of individuals in the spatial network to support decision-making during the virus spread process. Experimental results showed that this framework has superior performance in optimizing COVID-19 testing decisions and effectively identifying virus carriers from the population. 

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4. Epidemic management and control through risk-dependent individual contact interventions

   https://doi.org/10.1371/journal.pcbi.1010171  

   Schneider, Tapio; Dunbar, Oliver R.; Wu, Jinlong; Böttcher, Lucas; Burov, Dmitry; Garbuno-Inigo, Alfredo; Wagner, Gregory L.; Pei, Sen; Daraio, Chiara; Ferrari, Raffaele; et al (June 2022, PLOS Computational Biology)

   Moreno, Yamir (Ed.)

   Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City are used to establish proof-of-concept. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population (≳ 75%) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption. 

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5. Exo-SIR: an epidemiological model to analyze the impact of exogenous spread of infection

   https://doi.org/10.1007/s41060-022-00334-z  

   Sivaraman, Nirmal Kumar; Gaur, Manas; Baijal, Shivansh; Muthiah, Sakthi Balan; Sheth, Amit (June 2022, International Journal of Data Science and Analytics)

   Epidemics like Covid-19 and Ebola have impacted people’s lives signifcantly. The impact of mobility of people across the countries or states in the spread of epidemics has been signifcant. The spread of disease due to factors local to the population under consideration is termed the endogenous spread. The spread due to external factors like migration, mobility, etc., is called the exogenous spread. In this paper, we introduce the Exo-SIR model, an extension of the popular SIR model and a few variants of the model. The novelty in our model is that it captures both the exogenous and endogenous spread of the virus. First, we present an analytical study. Second, we simulate the Exo-SIR model with and without assuming contact network for the population. Third, we implement the Exo-SIR model on real datasets regarding Covid-19 and Ebola. We found that endogenous infection is infuenced by exogenous infection. Furthermore, we found that the Exo-SIR model predicts the peak time better than the SIR model. Hence, the Exo-SIR model would be helpful for governments to plan policy interventions at the time of a pandemic. Keywords Covid-19, Ebola, Epidemic modeling, Compartment model, Exogenous infection, Endogenous infection, SIR, Exo-SIR 

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