The COVID-19 outbreak has caused an unprecedented global reaction with countries taking drastic steps to combat the pandemic. Mathematical modeling and multi-agent based analysis of the pandemic allows better understanding of the disease spread and may help inform policy at the national and regional level. We use tools and modeling techniques from AI to help understand the situation better and design aids that may help policymakers design better solutions in the fight against this pandemic.
Cohorting to isolate asymptomatic spreaders: An agent-based simulation study on the Mumbai Suburban Railway
Published in International Conference on Autonomous Agents and Multi Agent Systems (AAMAS) as a short paper 2021. [paper] [pre-print]
The Mumbai Suburban Railways, locals, are a key transit infrastructure of the city and is crucial for resuming normal economic activity. Due to high density during transit, the potential risk of disease transmission is high, and the government has taken a wait and see approach to resume normal operations. To reduce disease transmission, policymakers can enforce reduced crowding and mandate wearing of masks. Cohorting - forming groups of travelers that always travel together, is an additional policy to reduce disease transmission on locals without severe restrictions. Cohorting allows us to: (i) form traveler bubbles, thereby decreasing the number of distinct interactions over time; (ii) potentially quarantine an entire cohort if a single case is detected, making contact tracing more efficient, and (iii) target cohorts for testing and early detection of symptomatic as well as asymptomatic cases. Studying impact of cohorts using compartmental models is challenging because of the ensuing representational complexity. Agent-based models provide a natural way to represent cohorts along with the representation of the cohort members with the larger social network. This paper describes a novel multi-scale agent-based model to study the impact of cohorting strategies on COVID-19 dynamics in Mumbai. We achieve this by modeling the Mumbai urban region using a detailed agent-based model comprising of 12.4 million agents. Individual cohorts and their inter-cohort interactions as they travel on locals are modeled using local mean field approximations. The resulting multi-scale model in conjunction with a detailed disease transmission and intervention simulator is used to assess various cohorting strategies. The results provide a quantitative trade-off between cohort size and its impact on disease dynamics and well being. The results show that cohorts can provide significant benefit in terms of reduced transmission without significantly impacting ridership and or economic & social activity.
Tracking disease outbreaks from sparse data with Bayesian inference
Published in AAAI Conference on Artificial Intelligence. 2021. [paper]
The COVID-19 pandemic provides new motivation for a classic problem in epidemiology: estimating the empirical rate of transmission during an outbreak (formally, the timevarying reproduction number) from case counts. While standard methods exist, they work best at coarse-grained national or state scales with abundant data, and struggle to accommodate the partial observability and sparse data common at finer scales (e.g., individual schools or towns). For example, case counts may be sparse when only a small fraction of infections are caught by a testing program. Or, whether an infected individual tests positive may depend on the kind of test and the point in time when they are tested. We propose a Bayesian framework which accommodates partial observability in a principled manner. Our model places a Gaussian process prior over the unknown reproduction number at each time step and models observations sampled from the distribution of a specific testing program. For example, our framework can accommodate a variety of kinds of tests (viral RNA, antibody, antigen, etc.) and sampling schemes (e.g., longitudinal or cross-sectional screening). Inference in this framework is complicated by the presence of tens or hundreds of thousands of discrete latent variables. To address this challenge, we propose an efficient stochastic variational inference method which relies on a novel gradient estimator for the variational objective. Experimental results for an example motivated by COVID-19 show that our method produces an accurate and well-calibrated posterior, while standard methods for estimating the reproduction number can fail badly.
Test sensitivity is secondary to frequency and turnaround time for COVID-19 screening
Published in Science Advances, 2020 [paper] [pre-print]
The COVID-19 pandemic has created a public health crisis. Because SARS-CoV-2 can spread from individuals with pre-symptomatic, symptomatic, and asymptomatic infections, the re-opening of societies and the control of virus spread will be facilitated by robust population screening, for which virus testing will often be central. After infection, individuals undergo a period of incubation during which viral titers are usually too low to detect, followed by an exponential viral growth, leading to a peak viral load and infectiousness, and ending with declining viral levels and clearance. Given the pattern of viral load kinetics, we model the effectiveness of repeated population screening considering test sensitivities, frequency, and sample-to-answer reporting time. These results demonstrate that effective screening depends largely on frequency of testing and the speed of reporting, and is only marginally improved by high test sensitivity. We therefore conclude that screening should prioritize accessibility, frequency, and sample-to-answer time; analytical limits of detection should be secondary.
Modeling between-population variation in COVID-19 dynamics in Hubei, Lombardy, and New York City
Published in Proceedings of the National Academy of Sciences [paper] [code] [updated code]
As the COVID-19 pandemic continues, formulating targeted policy interventions that are informed by differential severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission dynamics will be of vital importance to national and regional governments. We develop an individual-level model for SARS-CoV-2 transmission that accounts for location-dependent distributions of age, household structure, and comorbidities. We use these distributions together with age-stratified contact matrices to instantiate specific models for Hubei, China; Lombardy, Italy; and New York City, United States. Using data on reported deaths to obtain a posterior distribution over unknown parameters, we infer differences in the progression of the epidemic in the three locations. We also examine the role of transmission due to particular age groups on total infections and deaths. The effect of limiting contacts by a particular age group varies by location, indicating that strategies to reduce transmission should be tailored based on population-specific demography and social structure. These findings highlight the role of between-population variation in formulating policy interventions. Across the three populations, though, we find that targeted “salutary sheltering” by 50% of a single age group may substantially curtail transmission when combined with the adoption of physical distancing measures by the rest of the population.
India, a country of 1.3 billion people ordered the biggest ever, nation-wide lockdown in recorded history on 24th March 2020, for three weeks. A severe lockdown, while expected to be effective in checking the spread of disease, also puts a lot of social and economic burden on a country. On the other hand, adopting simple physical distancing strategies allows some return to normalcy, but might lead to widespread spread of infection. Choosing the optimal policy to adopt is thus a tricky task. In this project, we aim to use agent based modeling to estimate policy outcomes and thus help recommend the best policy decisions to take. We analyze the two specific states of Maharashtra and Uttar Pradesh in India and tune our model for analyzing policy implications in these settings.
Simulating Lockdown and Reopening Policies for US States [paper]
The United States has been particularly hard-hit by the COVID-19 pandemic, accounting for about one third of all reported cases and more than one quarter of all reported deaths worldwide as of May 10, 2020. To combat the spread, most states initiated full lockdown or shelter in place orders for a month or more. Now, as many states start to ease lockdowns, we run agent based simulations to inform policy decisions regarding reopening businesses, maintaining necessary levels of physical distancing, and taking decisive early action in the event of a second wave of infection.
Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics [paper]
- Coverage of the paper, "Surveillance testing of SARS-CoV-2" in the following venues:
- The New York Times, August 2020: [article]
- The Hill, August 2020: [article]
- Science, August 2020: [article]
- Clear Health Costs, August 2020: [article]
- El Confidencial, July 2020: [article]
- Time, July 2020: [article]
- USA today, July 2020: [article]
- The New York Times, July 2020: [article]
- USA today, July 2020: [article]
Other News Coverage:
- KNX 1070 Los Angeles Radio interview, May 2020: [Interview recording]
- India New England News, May 2020: "What is the right strategy to limit the spread of COVID-19?"
- Sakal Media House, May 2020: Middle ground for India's lockdown situation (in Marathi)
- Harvard University news and Events, May 2020: "What is the right strategy to limit the spread of COVID-19?"
- Interview on ABC-7 WJLA: "Using agent-based simulations to model the spread of COVID-19"
- Nature India: "Model finds middle ground for India's lockdown exit"
- The Daily Beast: "New Model Shows How Deadly Lifting Georgia's Lockdown May Be"