Background: The COVID-19 outbreak has already caused significant mortality worldwide. As the epidemic accelerates, understanding the transmission dynamics of COVID-19 is crucial to informing national and regional policies. We develop an individual-level model for SARS-CoV2 transmission which accounts for location-dependent distributions of age and household structure. We apply our model to Hubei, China and Lombardy, Italy to analyze the impact of demographic structure on estimates for key parameters such as the rate of documentation and the reproduction number r0 for COVID-19 cases. We also assess the effectiveness of potential policies ranging from physical distancing to sheltering in place in Lombardy.
Methods: Our study develops a stochastic, agent-based model for SARS-CoV2 spread. A key feature of the model is the inclusion of population-specific demographic structure, such as the distributions of age, household structure, contact across age groups, and comorbidities. We use prior estimates of these demographic features to instantiate our model for two locations: Hubei, China and Lombardy, Italy. Furthermore, we utilize the data on the number of reported deaths due to COVID-19 in both locations to estimate parameters describing location-specific variation in the transmissibility and fatality of the disease (for reasons beyond demography). The range of the parameters in our model that are consistent with reported data are used to construct plausible ranges for r0 and the rate of documentation in each location. Finally, we analyze potential policy responses in the context of Lombardy. Our analysis traces out the trade-off between adoption of physical distancing across the entire population and policies that encourage members of a specific age group to shelter at home.
Results: Our estimates for r0 are comparable to the rest of the literature, with a range of 2.11–2.27 for Hubei and 2.50-3.20 for Lombardy, suggesting higher rates of transmission in the latter. Scenarios where the case fatality rates are higher in Lombardy than Hubei by a factor of 1-5 times appear plausible given the data (even after accounting for differences in age and comorbidity distributions). We estimate the rate at which symptomatic cases are documented to be at 10.3-19.2% in Hubei and 1.2-8% in Lombardy, indicating that the number of undocumented cases may be even higher than has previously been estimated. Evaluation of potential policies suggests that encouraging a single age group to shelter in place is insufficient to control the epidemic by itself, but that targeted "salutary sheltering" by even 50% of a single age group has a substantial impact when combined with adoption of physical distancing by the rest of the population.
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  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.
Controlling recurrent infectious diseases is a vital yet complicated
problem in global health. During the long period of time from patients becoming infected to finally seeking treatment, their close
contacts are exposed and vulnerable to the disease they carry. Active screening (or case finding) methods seek to actively discover
undiagnosed cases by screening contacts of known infected people to reduce the spread of the disease. Existing practice of active
screening methods often screen all contacts of an infected person,
requiring a large budget. In cooperation with a research institute
in India, we develop a model of the active screening problem and
present a software agent, REMEDY. This agent assists maximizing
effectiveness of active screening under real world budgetary constraints and limited contact information. Our contributions are: (1)
A new approach to modeling multi-round network-based screening/contact tracing under uncertainty and proof of its NP-hardness;
(2) Two novel algorithms, Full- and Fast-REMEDY. Full-REMEDY
considers the effect of future actions and provides high solution
quality, whereas Fast-REMEDY scales linearly in the size of the
network; (3) Evaluation of Full- and Fast-REMEDY on several
real-world datasets which emulate human contact to show that
they control diseases better than the baselines. We also show that
the software agent is robust to errors in estimates of disease parameters, and incomplete information of the contact network. Our
software agent is currently under review before deployment as a
means to improve the efficiency of district-wise active screening
for tuberculosis in India.