Projecting hospital utilization during the COVID-19 outbreaks in the United States

Data deposition: The computational system is available in Github (https://github.com/affans/ncov2019odemodel).In the wake of community coronavirus disease 2019 (COVID-19) transmission in the United States, there is a growing public health concern regarding the adequacy of resources to treat infected cases. Hospital beds, intensive care units (ICUs), and ventilators are vital for the treatment of patients with severe illness. To project the timing of the outbreak peak and the number of ICU beds required at peak, we simulated a COVID-19 outbreak parameterized with the US population demographics. In scenario analyses, we varied the delay from symptom onset to self-isolation, the proportion of symptomatic individuals practicing self-isolation, and the basic reproduction number R0. Without self-isolation, when R0 =2.5, treatment of critically ill individuals at the outbreak peak would require 3.8 times more ICU beds than exist in the United States. Self-isolation by 20% of cases 24 h after symptom onset would delay and flatten the outbreak trajectory, reducing the number of ICU beds needed at the peak by 48.4% (interquartile range 46.4-50.3%), although still exceeding existing capacity. When R0 =2, twice as many ICU beds would be required at the peak of outbreak in the absence of self-isolation. In this scenario, the proportional impact of self-isolation within 24 h on reducing the peak number of ICU beds is substantially higher at 73.5% (interquartile range 71.4-75.3%). Our estimates underscore the inadequacy of critical care capacity to handle the burgeoning outbreak. Policies that encourage self-isolation, such as paid sick leave, may delay the epidemic peak, giving a window of time that could facilitate emergency mobilization to expand hospital capacity.S.M.M. acknowledges support from the Canadian Institutes of Health Research (grant OV4-170643; Canadian 2019 Novel Coronavirus Rapid Research), and the Natural Sciences and Engineering Research Council of Canada. A.P.G. gratefully acknowledges funding from the NIH (grant UO1-GM087719), the Burnett and Stender families’ endowment, the Notsew Orm Sands Foundation, NIH grant 1R01AI151176-01, and National Science Foundation grant RAPID-2027755. M.C.F. was supported by the NIH grant K01 AI141576.Integrative Biolog

Asymptomatic SARS-CoV-2 infection: a systematic review and meta-analysis

We aim to conduct a systematic review and meta-analysis of COVID-19 literature reporting laboratory-confirmed infections to determine the burden of silent infections, composed of presymptomatic and asymptomatic infections. For both, we aim to remove index cases from our calculations to avoid conflation

Replication Data for: Unraveling the disease consequences and mechanisms of modular structure in animal social networks

The dataset and link to codes used in our paper "Unraveling the disease consequences and mechanisms of modular structure in animal social networks". Note: The code for generating random modular graphs can be accessed from https://github.com/bansallab/modular_graph_generator. Additional codes to replicate the figures in the paper can be found at: https://github.com/bansallab/modularity_disease_implications The animal social networks used in the paper are available at: https://bansallab.github.io/asnr/</p

Replication Data for: Disease implications of animal social network structure: a synthesis across social systems

The dataset used in our paper "Disease implications of animal social network structure: a synthesis across social systems". Note: The code for disease simulations can be accessed from https://github.com/prathasah/simulate_epidemic. The animal social networks used in the paper are available at: https://bansallab.github.io/asnr

Replication Data for: Optimizing impact of low-efficacy influenza vaccines

The dataset associated with our paper: Sah P, Medlock J, Fitzpatrick MC, Singer BH, Galvani AP. Optimizing impact of low-efficacy influenza vaccines. Proc Natl Acad Sci USA. 2018. Note: The associate code can be accessed from https://github.com/prathasah/optimizing-flu-vaccine Please cite the paper above, if you use our data or code in any form or create a derivative work

Replication Data for: Inferring social structure and its drivers from refuge use in the desert tortoise, a relatively solitary species

Data used for burrow switching and burrow popularity regression model

Stabilizing spatially-structured populations through adaptive Limiter Control.

Stabilizing the dynamics of complex, non-linear systems is a major concern across several scientific disciplines including ecology and conservation biology. Unfortunately, most methods proposed to reduce the fluctuations in chaotic systems are not applicable to real, biological populations. This is because such methods typically require detailed knowledge of system specific parameters and the ability to manipulate them in real time; conditions often not met by most real populations. Moreover, real populations are often noisy and extinction-prone, which can sometimes render such methods ineffective. Here, we investigate a control strategy, which works by perturbing the population size, and is robust to reasonable amounts of noise and extinction probability. This strategy, called the Adaptive Limiter Control (ALC), has been previously shown to increase constancy and persistence of laboratory populations and metapopulations of Drosophila melanogaster. Here, we present a detailed numerical investigation of the effects of ALC on the fluctuations and persistence of metapopulations. We show that at high migration rates, application of ALC does not require a priori information about the population growth rates. We also show that ALC can stabilize metapopulations even when applied to as low as one-tenth of the total number of subpopulations. Moreover, ALC is effective even when the subpopulations have high extinction rates: conditions under which another control algorithm had previously failed to attain stability. Importantly, ALC not only reduces the fluctuation in metapopulation sizes, but also the global extinction probability. Finally, the method is robust to moderate levels of noise in the dynamics and the carrying capacity of the environment. These results, coupled with our earlier empirical findings, establish ALC to be a strong candidate for stabilizing real biological metapopulations

Effects of increasing the fraction of ALC controlled subpopulation on metapopulation constancy.

<p>In this figure, each metapopulation consists of 10 subpopulations. For low values of <i>c</i>, increasing the fraction of perturbed subpopulations can have a negative effect on constancy. Error bars denote ±SEM and are too small to be visible.</p

Effects of ALC on constancy in metapopulations with different number of subpopulations.

<p>(<b>A</b>) LALC (i.e. c = 0.25), and (<b>B</b>) HALC (i.e. c = 0.4). In both figures, only one subpopulation is perturbed for increasing number of subpopulations. Perturbing only 1 patch by ALC can reduce FI of metapopulations with up to 10 subpopulations. Error bars denote ±SEM and are too small to be visible.</p