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COVID Open Source Models

COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions (Silva et al)

The COVID-19 pandemic due to the SARS-CoV-2 coronavirus has directly impacted the public health and economy worldwide. To overcome this problem, countries have adopted different policies and non-pharmaceutical interventions for controlling the spread of the virus. This paper proposes the COVID-ABS, a new SEIR (Susceptible-Exposed-Infected-Recovered) agent based model that aims to simulate the pandemic dynamics using a society of agents emulating people, business and government. Seven different scenarios of social distancing interventions were analyzed, with varying epidemiological and economic effects: (1) do nothing, (2) lockdown, (3) conditional lockdown, (4) vertical isolation, (5) partial isolation, (6) use of face masks, and (7) use of face masks together with 50% of adhesion to social isolation. In the impossibility of implementing scenarios with lockdown, which present the lowest number of deaths and highest impact on the economy, scenarios combining the use of face masks and partial isolation can be the more realistic for implementation in terms of social cooperation. The COVID-ABS model was implemented in Python programming language, with source code publicly available. The model can be easily extended to other societies by changing the input parameters, as well as allowing the creation of a multitude of other scenarios. Therefore, it is a useful tool to assist politicians and health authorities to plan their actions against the COVID-19 epidemic.

COVID-19 Hospital Impact Model for Epidemics by Penn Medicine, The CHIME (COVID-19 Hospital Impact Model for Epidemics)

Application is designed to assist hospitals and public health officials with understanding hospital capacity needs as they relate to the COVID pandemic. CHIME enables capacity planning by providing estimates of total daily (i.e. new) and running totals of (i.e. census) inpatient hospitalizations, ICU admissions, and patients requiring ventilation. These estimates are generated using a SIR (Susceptible, Infected, Recovered) model, a standard epidemiological modeling technique.

COVID-19 Multi-Model Comparison Collaboration (CMCC) policy and model comparison reports by CMCC

The aim of the CMCC is to provide researchers and decision-makers (with a focus on LMICs) with an accessible overview of aims, capabilities and limits of the existing COVID-19 models, as well as how their projections differ and what the models’ key assumptions and drivers are. The reports intend to support users better to interpret the estimates from these tools for planning and strategic decisions. The policy report was prepared with policy-makers planning their COVID-19 response in countries to capture their intention and experience with using models, identify relevant decision and evidence needs, as well as understanding their perspectives on how to effectively report and communicate models to maximise impact.

AdaptER-Covid19 by Pathogen Dynamics group (at the Big Data Institute at the University of Oxford, in conjunction with IBM UK and Faculty)

AdaptER-covid19, an economics model, is attached to the main OpenABM-Covid19 model so the economic effect of Covid-19 can be modelled jointly with the spread of the disease. AdaptER-Covid19 is intended to be used as part of the wider OpenABM-Covid19 model.

COVID scenario pipeline by Johns Hopkins University Infectious Disease Dynamics COVID-19 Working Group

A flexible modeling framework that projects epidemic trajectories and healthcare impacts under different suites of interventions in order to aid in scenario planning. The model is generic enough to be applied to different spatial scales given shapefiles, population data, and COVID-19 confirmed case data. There are multiple components to the pipeline, which may be characterized as follows: 1) epidemic seeding; 2) disease transmission and non-pharmaceutical intervention scenarios; 3) calculation of health outcomes (hospital and ICU admissions and bed use, ventilator use, and deaths); and 4) summarization of model outputs.

LSHTM Covid M App by LSHTM

This app uses an age-structured mathematical model developed by researchers at the London School of Hygiene and Tropical Medicine that simulates SARS-CoV-2 transmission in a population. The user can set the country, virus introduction date, initial number of infections, basic reproduction rate, and risk of death and hospitalisation using their own assumptions/estimates for those parameters. The user can also set different interventions such as social distancing (intensity, duration). This will allow them to produce results for the number of cases, deaths from COVID-19 as well as the number of hospital beds occupied with the set parameters and interventions.

LMICs short-term forecasts dashboard by Imperial College London

The tool provide each country with an indication of where they are in their epidemic and scenarios of how healthcare demand is likely to vary over the next 28 days, according to the following indicators: 1. The total number of COVID-19 infections, 2. The expected number of deaths within the next 28 days, 3. The number of individuals requiring oxygen or mechanical ventilation in the next 28 days, 4. The impact of changing their current intervention policy.

COVID-19 Scenario Analysis Tool  by Imperial College London

This tool allows the user to make projections of the prevalence of infections each day and the expected number of people requiring hospitalisaiton and critical care facilities.

Expanded tool to estimate net health impact of Covid-19 policies by Center for Global Development)

This new version of the tool allows users to compare mortality estimates from three main models (ICL, WHO Afro, and now LSHTM), with estimates of indirect health impacts (generated by end users in the tool) using Global Burden of Disease (GBD) data. By doing so, users can make an educated guesstimate of the net health impacts.

The Health and Economic Impacts of COVID-19 Interventions  by Rand

This tool supports decisionmakers in planning a recovery roadmap by estimating the effects of nonpharmaceutical interventions on health and economic outcomes. The tool also provides qualitative guidance on the efficacy, costs, and potential unintended consequences of a range of interventions. The tool draws on an epidemiological model and an economic model to estimate effects, based on evidence from past epidemics, peer-reviewed literature, and data from the current pandemic. Data on current impacts are updated daily where available.

LSHTM CMMID: COVID-19 transmission model by LSHTM

This app uses an age-structured mathematical model developed by researchers at the London School of Hygiene and Tropical Medicine that simulates SARS-CoV-2 transmission in a population [1,2]. It assumes that people infected with SARS-CoV-2 can either develop clinical symptoms (clinical infections) or only develop mild or no symptoms (subclinical infections), in which case the infection goes unnoticed. Children are assumed to be less susceptible to infection, and less likely to show clinical symptoms, than adults. The model also estimates the number of deaths from COVID-19 [3] as well as the number of hospital beds occupied. A full explanation of the model can be found in the paper ‘Age-dependent effects in the transmission and control of COVID-19 epidemics’ [1]. Results from this model are being used by public health experts and policymakers in the UK [2] and around the world.

LSHTM CMMID: Inferring COVID-19 cases from deaths model by LHSTM

Aims to estimate the numbers of circulating COVID-19 cases from recently reported deaths, in places where surveillance has not revealed COVID-19 infections yet.

LSHTM CMMID: hospital bed capacity forecast by LSHTM

Forecast bed occupancy for COVID-19 patients using recent data on admission and an exponential growth model.