COVID-19

2021
Alok Talekar, Sharad Shriram, Nidhin Vaidhiyan, Gaurav Aggarwal, Jiangzhuo Chen, Srini Venkatramanan, Lijing Wang, Aniruddha Adiga, Adam Sadilek, Ashish Tendulkar, Madhav Marathe, Rajesh Sundaresan, and Milind Tambe. 5/2021. “Cohorting to isolate asymptomatic spreaders: An agent-based simulation study on the Mumbai Suburban Railway.” In 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS) as a Short Paper). Publisher's Version 20210212_talekar_cohort_crc.pdf
Bryan Wilder, Michael Mina, and Milind Tambe. 2021. “Tracking disease outbreaks from sparse data with Bayesian inference.” In AAAI Conference on Artificial Intelligence.Abstract

The COVID-19 pandemic provides new motivation for a classic problem in epidemiology: estimating the empirical rate of transmission during an outbreak (formally, the time-varying 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.

aaai_rt.pdf
2020
Ankit Bhardwaj*, Han Ching Ou*, Haipeng Chen, Shahin Jabbari, Milind Tambe, Rahul Panicker, and Alpan Raval. 11/2020. “Robust Lock-Down Optimization for COVID-19 Policy Guidance.” In AAAI Fall Symposium. robust_lock-down_optimization_for_covid-19_policy_guidance.pdf
Daniel B. Larremore, Bryan Wilder, Evan Lester, Soraya Shehata, James M. Burke, James A. Hay, Milind Tambe, Michael J. Mina, and Roy Parker. 11/2020. “Test sensitivity is secondary to frequency and turnaround time for COVID-19 screening.” Science Advances. Publisher's VersionAbstract
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.
scienceadvances_full_.pdf
Bryan Wilder, Marie Charpignon, Jackson A Killian, Han-Ching Ou, Aditya Mate, Shahin Jabbari, Andrew Perrault, Angel Desai, Milind Tambe, and Maimuna S. Majumder. 9/24/2020. “Modeling between-population variation in COVID-19 dynamics in Hubei, Lombardy, and New York City.” Proceedings of the National Academy of Sciences. Publisher's Version pnas_full.pdf
Evaluating COVID-19 Lockdown and Business-Sector-Specific Reopening Policies for Three US States
Jackson A. Killian, Marie Charpignon, Bryan Wilder, Andrew Perrault, Milind Tambe, and Maimuna S. Majumder. 8/24/2020. “Evaluating COVID-19 Lockdown and Business-Sector-Specific Reopening Policies for Three US States.” In KDD 2020 Workshop on Humanitarian Mapping. Publisher's VersionAbstract
Background: The United States has been particularly hard-hit by COVID-19, accounting for approximately 30% of all global cases and deaths from the disease that have been reported as of May 20, 2020. We extended our agent-based model for COVID-19 transmission to study the effect of alternative lockdown and reopening policies on disease dynamics in Georgia, Florida, and Mississippi. Specifically, for each state we simulated the spread of the disease had the state enforced its lockdown approximately one week earlier than it did. We also simulated Georgia's reopening plan under various levels of physical distancing if enacted in each state, making projections until June 15, 2020.

Methods: We used an agent-based SEIR model that uses population-specific age distribution, household structure, contact patterns, and comorbidity rates to perform tailored simulations for each region. The model was first calibrated to each state using publicly available COVID-19 death data as of April 23, then implemented to simulate given lockdown or reopening policies.

Results: Our model estimated that imposing lockdowns one week earlier could have resulted in hundreds fewer COVID-19-related deaths in the context of all three states. These estimates quantify the effect of early action, a key metric to weigh in developing prospective policies to combat a potential second wave of infection in each of these states. Further, when simulating Georgia’s plan to reopen select businesses as of April 27, our model found that a reopening policy that includes physical distancing to ensure no more than 25% of pre-lockdown contact rates at reopened businesses could allow limited economic activity to resume in any of the three states, while also eventually flattening the curve of COVID-19-related deaths by June 15, 2020.
covid_19_us_states.pdf
Aditya Mate, Jackson A. Killian, Bryan Wilder, Marie Charpignon, Ananya Awasthi, Milind Tambe, and Maimuna S. Majumder. 8/2020. “Evaluating COVID-19 Lockdown Policies for India: A Preliminary Modeling Assessment for Individual States.” KDD 2020 Workshop on Humanitarian Mapping.Abstract
Background: On March 24, India ordered a 3-week nationwide lockdown in an effort to control the spread of COVID-19. While the lockdown has been effective, our model suggests that completely ending the lockdown after three weeks could have considerable adverse public health ramifications. We extend our individual-level model for COVID-19 transmission [1] to study the disease dynamics in India at the state level for Maharashtra and Uttar Pradesh to estimate the effect of further lockdown policies in each region. Specifically, we test policies which alternate between total lockdown and simple physical distancing to find "middle ground" policies that can provide social and economic relief as well as salutary population-level health effects. Methods: We use an agent-based SEIR model that uses population-specific age distribution, household structure, contact patterns, and comorbidity rates to perform tailored simulations for each region. The model is first calibrated to each region using publicly available COVID-19 death data, then implemented to simulate a range of policies. We also compute the basic reproduction number R0 and case documentation rate for both regions. Results: After the initial lockdown, our simulations demonstrate that even policies that enforce strict physical distancing while returning to normal activity could lead to widespread outbreaks in both states. However, "middle ground" policies that alternate weekly between total lockdown and physical distancing may lead to much lower rates of infection while simultaneously permitting some return to normalcy.
kdd_workshop_india_modeling_cam_ready.pdf
Daniel B Larremore, Bryan Wilder, Evan Lester, Soraya Shehata, James M Burke, James A Hay, Milind Tambe, Michael J Mina, and Roy Parker. 6/25/2020. “Surveillance testing of SARS-CoV-2.” medRxiv. Publisher's VersionAbstract
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 surveillance, 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 growth of virus, 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 surveillance effectiveness considering test sensitivities, frequency, and sample-to-answer reporting time. These results demonstrate that effective surveillance, including time to first detection and outbreak control, depends largely on frequency of testing and the speed of reporting, and is only marginally improved by high test sensitivity. We therefore conclude that surveillance should prioritize accessibility, frequency, and sample-to-answer time; analytical limits of detection should be secondary.
2020.06.22.20136309v1.full_.pdf
Aniruddha Adiga, Lijing Wang, Adam Sadilek, Ashish Tendulkar, Srinivasan Venkatramanan, Anil Vullikanti, Gaurav Aggarwal, Alok Talekar, Xue Ben, Jiangzhuo Chen, Bryan Lewis, Samarth Swarup, Milind Tambe, and Madhav Marathe. 6/5/2020. “Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics”. Publisher's Version merrxiv.pdf
Aditya Mate, Jackson A. Killian, Bryan Wilder, Marie Charpignon, Ananya Awasthi, Milind Tambe, and Maimuna S. Majumder. 4/13/2020. “Evaluating COVID-19 Lockdown Policies For India: A Preliminary Modeling Assessment for Individual States.” SSRN. Publisher's VersionAbstract
Background: On March 24, India ordered a 3-week nationwide lockdown in an effort to control the spread of COVID-19. While the lockdown has been effective, our model suggests that completely ending the lockdown after three weeks could have considerable adverse public health ramifications. We extend our individual-level model for COVID-19 transmission [1] to study the disease dynamics in India at the state level for Maharashtra and Uttar Pradesh to estimate the effect of further lockdown policies in each region. Specifically, we test policies which alternate between total lockdown and simple physical distancing to find "middle ground" policies that can provide social and economic relief as well as salutary population-level health effects.

Methods: We use an agent-based SEIR model that uses population-specific age distribution, household structure, contact patterns, and comorbidity rates to perform tailored simulations for each region. The model is first calibrated to each region using publicly available COVID-19 death data, then implemented to simulate a range of policies. We also compute the basic reproduction number R0 and case documentation rate for both regions.

Results: After the initial lockdown, our simulations demonstrate that even policies that enforce strict physical distancing while returning to normal activity could lead to widespread outbreaks in both states. However, "middle ground" policies that alternate weekly between total lockdown and physical distancing may lead to much lower rates of infection while simultaneously permitting some return to normalcy.
ssrn-covid_lockdown_policies_india.pdf