Prediction and prevention of pandemics via graphical model inference and convex programming

Abstract

Hard-to-predict bursts of COVID-19 pandemic revealed significance of statistical modeling which would resolve spatio-temporal correlations over geographical areas, for example spread of the infection over a city with census tract granularity. In this manuscript, we provide algorithmic answers to the following two inter-related public health challenges of immense social impact which have not been adequately addressed (1) Inference Challenge assuming that there are N census blocks (nodes) in the city, and given an initial infection at any set of nodes, e.g. any N of possible single node infections, any $$N(N-1)/2$$ N ( N - 1 ) / 2 of possible two node infections, etc, what is the probability for a subset of census blocks to become infected by the time the spread of the infection burst is stabilized? (2) Prevention Challenge What is the minimal control action one can take to minimize the infected part of the stabilized state footprint? To answer the challenges, we build a Graphical Model of pandemic of the attractive Ising (pair-wise, binary) type, where each node represents a census tract and each edge factor represents the strength of the pairwise interaction between a pair of nodes, e.g. representing the inter-node travel, road closure and related, and each local bias/field represents the community level of immunization, acceptance of the social distance and mask wearing practice, etc. Resolving the Inference Challenge requires finding the Maximum-A-Posteriory (MAP), i.e. most probable, state of the Ising Model constrained to the set of initially infected nodes. (An infected node is in the $$+ \, 1$$ + 1 state and a node which remained safe is in the $$- \, 1$$ - 1 state.) We show that almost all attractive Ising Models on dense graphs result in either of the two possibilities (modes) for the MAP state: either all nodes which were not infected initially became infected, or all the initially uninfected nodes remain uninfected (susceptible). This bi-modal solution of the Inference Challenge allows us to re-state the Prevention Challenge as the following tractable convex programming: for the bare Ising Model with pair-wise and bias factors representing the system without prevention measures, such that the MAP state is fully infected for at least one of the initial infection patterns, find the closest, for example in $$l_1$$ l 1 , $$l_2$$ l 2 or any other convexity-preserving norm, therefore prevention-optimal, set of factors resulting in all the MAP states of the Ising model, with the optimal prevention measures applied, to become safe. We have illustrated efficiency of the scheme on a quasi-realistic model of Seattle. Our experiments have also revealed useful features, such as sparsity of the prevention solution in the case of the $$l_1$$ l 1 norm, and also somehow unexpected features, such as localization of the sparse prevention solution at pair-wise links which are NOT these which are most utilized/traveled.

Practical AI Cases for Solving ESG Challenges

“…It is assumed that AI may be beneficial for 82% of social SDG targets (Figure 1), for example, SDG 1-eliminating poverty, SDG 4-quality education, and SDG 6-clean water and sanitation [15]. The recent SARS-CoV-2 pandemic also highlighted the necessity of disease spread modeling [43]. As can be seen from Figure 1, some society SDG targets are also tied to the environment sector, in particular, smart cities.…”

Diffusion and Prediction of Covid-19 Pandemic: Limits of Models and Strategies to Improve Outlook and Preparedness to Face Next Pandemics

“…Hence, since the forecasting of pandemics in the short and long run using current epidemiologic models, as all human activity of prediction, has manifold shortcomings and can provide misleading results, the main goals of this study are twofold: first, the analysis of anthropogenic activities and factors that may trigger pandemic threats; the planning of optimal strategies to reduce the hazards and risks of emergence of pandemic threat and/or in the initial phase to reduce negative impact associated with the emergence and diffusion of new viruses that can generate problems on public health, environment and socioeconomic systems. In particular, the investigation and understanding of sources and driving factors concerning the emergence and diffusion of new pandemics have critical aspects for designing appropriate strategic actions of prevision, prevention and planning of effective policy responses to improve preparedness to cope with next pandemic crises and health emergencies (Coccia, 2022c;Dai et al, 2022;Krechetov et al, 2022;Kuvvetli et al, 2021;Liu et al, 2022;Šušteršič et al, 2021). Hence, this study endeavors, whenever possible, to clarify these problems to increase the knowledge of the sources and factor determining the emergence of new viral agents in order to design optimal response policies to face next pandemic diseases similar to COVID-19 (Farazmand, 2001(Farazmand, , 2014.…”