To improve transparency, the Centre has created a catalogue of predictive models with information on ‘who is doing what, where and when’. We hope that this effort will make it easier for partners to get a quick overview of the models that are available and their current state of development. If your organization would like to be listed in the catalogue, please fill in this form. Learn more about the Centre’s Peer Review Framework for Predictive Models.

Model name Partners involved * Topic * Geographical scope * Description Update frequency Link to model code (if available) Main contacts Additional documentation
Project Jetson UNHCR Innovation Service, UN Global Pulse, Essex University Big Data & Technology Centre, Omdena Foundation Affected people Somalia, Ethiopia Jetson is experimenting with two methods: gravity model and multivariate TSA, machine learning-based (open source, Python and R applications). The target variable is forced displacement, and the nexus between displacement, climate/weather anomalies and changes and violent conflict. The model predicts at least one month of forced displacement movement (population flow, influx-only). This is the arrivals of IDPs and Refugees in the different regions of Somalia (admin level 1) and the southern border region of Ethiopia (Dollo Ado). Every 3 months since 2017 https://github.com/unhcr/Jetson innovation@unhcr.org http://jetson.unhcr.org/
Unaccompanied and separated children projection model UNHCR, University of East Anglia Affected people South Sudan, Democratic Republic of the Congo, Somalia Arrivals of unaccompanied and separated children in refugee settings. The outcome is a prediction of the number of arrivals of Unaccompanied and Separated Children (UASC), usually by month but sometimes by week. Under development - models created for several countries and updated each year. innovation@unhcr.org
The Joint Analysis of Disaster Exposure (JADE) OCHA (Asia-Pacific), WFP (Asia-Pacific), Pacific Disaster Centre Affected people 20 countries supported in the Asia Pacific region Estimates the number of people likely to be in need after a sudden onset natural hazard. The JADE provides the number of people living in affected areas, the number of people living in the worst affected areas, and the number of people living in the worst affected area who were already considered "vulnerable" before the crisis. There are a number other values and figures provided. The spatial resolution is 1 x 1 km. The initiative was launched in 2018. The actual analysis is launched within 24 hours of a sudden onset emergency. It is not expected to be updated during the course of an emergency. https://www.pdc.org/contact/
Flee Brunel University London Affected people The results cover case studies of three African countries at the moment but can be generalized to more contexts as per the authors. Flee aims to establish a tool that can (a) forecast the destinations of forcibly displaced persons, (b) estimate the effects on migration of major policy decisions such as border closures and camp relocations, (c) provide an estimation of the number of displaced persons in areas where the data collection is incomplete. The first major version of Flee was established in 2017. It is updated continuously and is funded until May 2023. An updated and more realistic rule set for agent movement is expected in Q1 or Q2 2020. https://github.com/djgroen/flee-release
INFORM Risk Index Joint Research Centre of European Commission, OCHA Coordination and context Global coverage (subnational in some countries) Risk for humanitarian crisis and disasters is evaluated along three dimensions coping capacity, vulnerability and hazard. The model has global coverage, and the output is national scale. It was developed in the 2014, and it is updated twice a year http://publications.europa.eu/resource/cellar/b1ef756c-5fbc-11e7-954d-01aa75ed71a1.0001.02/DOC_1 contact@inform-index.org
Foresight model Danish Refugee Council, IBM Refugees and persons of concern Afghanistan, Myanmar The project develops a forecasting model for total forced displacement from a country 1-3 years ahead. Total forced displacement is a combination of refugees and asylum seekers outside a given country and the internally displaced in the same country. The project was updated in 2019-2020 and it will be continuously updated with more countries. https://github.com/IBM/mixed-migration-forecasting drc@drc.ngo OCHA-Bucky model card according to the Centre's Peer Review Framework.
The Inundation Model - Google flood forecasting model Google AI for Social Good Affected areas Pilot in India (Bihar) with extended coverage in in 2019 in the Indian subcontinent. Simulation based real time flood forecasting model. Accurate predictions of severity and locations of flooding based on AI and physics simulation. Integrated with local partners to put an active warning system in place. Project started mid 2018 and latest 2019 iteration available online. https://ai.googleblog.com/2019/03/a-summary-of-google-flood-forecasting.html
African Risk Capacity African Union, Africa Risk Capacity Affected people 33 African Union member states Large umbrella effort to have better responses towards extreme weather events and natural disasters. The frameworks uses predictive analytics and innovative financing (insurance to redistribute risk and risk pooling) to respond. Framework covers multiple disasters types: droughts, floods, diseases, and cyclones. The pure modelling efforts are ARV (Africa riskview). ARV model updated every year (sometimes for every harvest season) Proprietary software package - access can be requested. Proprietary software. Model details available here info@africanriskcapacity.org
Famine Early Warning Systems Network (FEWSNET) USAID, NASA, NOAA, USDA, USGS Food security 28 countries with a focus on West, Southern and East Africa Early warning and analysis of food insecurity - livelihood based model. Efforts are made to understand impacts on livelihoods, markets/sources, nutrition. A key component of monitoring is satellite based remote sensing. Mixed methods analysis (quantitative and qualitative analysis) enables FEWSNET to build reliable scenarios and Integrated Food Classification (IPC). Updates may be issued every month. Consistent new datasets for all monitored countries are released every ~6 months (every harvest season) https://fews.net/contact
Artemis Model World Bank Food security Pilot in 5 countries to be expanded to 21 additional countries The model combines qualitative, remote sensing, and data insights from multiple sources to generate a subnational signal of food insecurity at least 6 months to 1 year in advance. No planned updates of the model after publication in scientific journal. Scientific paper to be published in 2020
Hunger Map LIVE WFP and Alibaba Food security Global coverage (90 countries) Advanced statistical/ML system with a focus on large scale nowcasting. The hunger map is a platform that uses streams of public data from multiple sources to create a complex dynamic subnational portrait of world hunger. In places where the data is missing ML algorithms are used to make extrapolations. Daily wfp.mvam@wfp.org https://hungermap.wfp.org/
Crisis Computing/ Artificial Intelligence for Digital Response Qatar Computing Research Institute Coordination and context Global coverage Natural Language processing model which focuses on extracting disaster and emergency related information from social media messages. The objective of the model is to gain actionable information from troves of social media to inform better humanitarian response In real time Part of code used in the crisis computing project is available online at the official github
Asset Impact Monitoring System (AIMS) WFP Geography and infrastructure Pilots in Niger, Aghanistan, Sudan, South Sudan, Tajikistan The objective of this system is to monitor the rehabilitation of degraded landscapes using satellite imagery and remote sensing. The AIMS model mainly monitors the degraded land which is rehabilitated with with the Food Assistance for Assets (FFA) program. This approach gives evidence based quantifiable impacts of policy and landscape changes. AIMS Model in development/pilot phase. Last public update was given in Oct 2018 for the 5 pilot countries. https://innovation.wfp.org/about-us
510 Typhoon impact model Netherlands Red Cross - 510 Coordination and context The Philippines Predictive model to identify high priority areas in the wake of a natural disaster. The model uses a large numbers of factors to assess this including but not limited to socio-economic factors, housing types, impact data and administrative boundaries. One fully validated model for Philippines available. Pilots in development to test model scale ability in Mozambique. https://github.com/rodekruis/Typhoon-Impact-based-forecasting-model/blob/master/models/four_models_bootstraping.R support@510.global
Mongolia Dzud model National Agency for Meteorology and Environmental Monitoring (NAMEM) Coordination and context Mongolia Each year, National Agency for Meteorology and Environmental Monitoring (NAMEM) publishes Dzud risk map which uses summer condition, pasture carrying capacity, livestock number, anomalous precipitation and temperature, snow depth, biomass, drought index, temperature forecast etc. to predict which regions may experience severe winter condition. Yearly https://media.ifrc.org/ifrc/fba/ https://t.co/1KAe9AtRDs?amp=1
REACH flood susceptibility model REACH Affected areas Yemen A model is build using high resolution data sets to account for topographical features, normalized vegetation index (NDVI), rainfall, drainage, land cover and soil type. These factors are combined in a weighted linear fashion to compute an area's flood susceptibility (not risk). Static model updated last in June 2019 yemen@reach-initiative.org
Global Disaster Displacement Risk Model IDMC, UNDRR, ETH Zurich Affected people Global Coverage The model combines probabilistic analysis of events recorded in national loss databases (retrospective risk) with probabilistic risk estimates for low-frequency, high-impact events (prospective risk). Prospective disaster displacement risk is expressed as a function of hazard, exposure and vulnerability: Risk = Exposure x Hazard x Vulnerability The prospective risk profile is calculated using hazard scenarios provided by a wide range of partners. These events represent all of the possible disasters that could affect a country over different return periods. For each grid cell in the exposure map, which has a resolution of between 1km x 1km in coastal areas to 5km x 5km inland, the expected impact of a hazard is calculated using vulnerability curves to determine an expected level of damage to homes and other structures based on the hazard’s intensity. Whenever the simulated damage to a home is more than 55 per cent, it is considered uninhabitable and the household displaced. Model developed in 2018 using from the 2015 Global Assessment Report (https://www.preventionweb.net/english/hyogo/gar/2015/en/home/ ) info@idmc.ch
Pastoralist Livelihood and Displacement Simulator IDMC, Climate Interactive Affected people Kenya, Ethiopia, Somalia System dynamics (a modelling technique often used to analyse population dynamics and the behaviour of complex systems) is used to understand displacement dynamics of pastoralists in the Horn of Africa. The model formalises the causal relationships and drivers of pastoralist displacement, providing a decision support tool to explore the impact of specific policies and interventions. The model user interface is designed to answer a decision-maker’s “what if?” questions. Developed in 2015 info@idmc.ch https://www.internal-displacement.org/sites/default/files/publications/documents/201405-horn-of-africa-technical-report-en.pdf
Global Cholera Risk Model (GCRM) University of Florida and University of Maryland Affected people Global coverage The algorithm uses precipitation, temperature, and population data from different data sources to project the risk of a cholera outbreak 4 weeks later. The risk values represent the conditions under which Vibrio’s are more likely to grow. Cholera risk increases if a region experiences a two month above average air temperature, followed by one month of above average precipitation. These two conditions must be accompanied by damage to WASH infrastructure, further facilitating interaction of cholera bacteria with humans and thereby leading to an outbreak. Daily, weekly and monthly updates available https://www.essie.ufl.edu/environmental-engineering-sciences/
PRIO Conflict Prediction Peace Research Institute Oslo (PRIO) Conflict Global Modelling effort that predicts internal armed conflict in a country. The scope of the study ranges from 1970 to 2050. The model uses a wide range of predictors from conflict history, development indicators of the country, neighborhood stability. The model subsequently goes through extensive testing and validation exercises. Last update in Jan 2013 http://folk.uio.no/hahegre/Papers/PredictionISQ_Final.pdf https://www.prio.org/Publications/Publication/?x=4978 https://www.prio.org/Projects/Project/?x=1401
ViEWS: Violence Early-Warning System Upsalla University - Department of Peace and Conflict Research Conflict Africa An early warning system to track and predict three classes of political violence. The model can predict armed conflict involving states and rebel groups, armed conflict between non-state actors, and violence against civilians. More importantly the model can disaggregate results into three different levels of analysis. We can analyse violence at national level, subnational level and actor level. The model takes into account a large array of input variable including but not limited to history of conflict, political institute, economic development, temporal factors, demography, natural resources and proximity of conflict. Every month https://www.pcr.uu.se/research/views/methodology/ views@pcr.uu.se Main Website - https://www.pcr.uu.se/research/views/ Current forecasts - https://www.pcr.uu.se/research/views/current-forecasts/ Peer reviewed publication - https://www.pcr.uu.se/digitalAssets/653/c_653796-l_1-k_croicuhegrekgi.pdf https://www.pcr.uu.se/digitalAssets/653/c_653796-l_1-k_pko_prediction_preprint_main.pdf
Conflict Early Warning and Response Mechanism (CEWARN) Intergovernmental Authority on Development (IGAD) Conflict Migration Djibouti, Eritrea, Ethiopia, Kenya, Somalia, South Sudan, Sudan, Uganda Information of conflicts, migrants and natural disasters is shared between participating countries in a transparent manner. Thus information about potentially violent situations are jointly analysed to develop case scenarios and optimal responses. The analysis, data and response options are also communicated to all IGAD member states https://micicinitiative.iom.int/sites/default/files/document/micic_guidelines_english_web_13_09_2016.pdf https://micicinitiative.iom.int/micicinitiative/conflict-early-warning-and-response-mechanism-cewarn
CrisisWatch The International Crisis Group Conflict Global coverage Crisis watch is a "nowcasting" tool that is updated every month to get an overview of security and political developments across 80 countries in almost real time. The tool provides a bird eye view of ongoing conflict trends, escalations and developmental impact for all decision makers. 1 month Main Website - https://www.crisisgroup.org/crisiswatch https://www.crisisgroup.org/about-crisiswatch
The Early Warning Project Simon-Skjodt Center and Dartmouth College Conflict Global coverage The statistical model of this project computes risk of mass killing and genocide by studying previous episodes (1945- present) to identify predictors and risk factors. Various models are cross validated and the best performing ones are then used to make predictions for the next two years. The output of a model is an estimated risk (of an onset of a mass killing) and the country's vulnerability rank. New predictions made every 2 years https://earlywarningproject.ushmm.org/methodology-statistical-model Main Website - https://earlywarningproject.ushmm.org/ https://earlywarningproject.ushmm.org/map
OCHA-Bucky Johns Hopkins University Applied Physics Laboratory, UN OCHA Centre for Humanitarian Data Affected people Completed: Afghanistan, Democratic Republic of the Congo, Iraq, Somalia, South Sudan, Sudan. The OCHA-Bucky model consists of a series of adjustments to an existing COVID-19 model (JHUAPL-Bucky) that was developed as part of the Centers for Disease Control and Prevention (CDC) COVID-19 Mathematical Modeling Forecasting Ensemble. OCHA-Bucky is a collection of coupled and stratified SEIR models. Since COVID-19 exhibits heavily age-dependent properties, wherein a majority of severe cases are in older individuals, SEIR models are stratified via the age demographic structure of a geographic region to obtain accurate estimates of case severity and deaths. Additionally, to model the spatial dynamics of COVID spread, we consider a set of SEIR sub-models at the smallest geographic level for which we have appropriate data. For a full description of the methodology please refer to the OCHA-Bucky methodology paper. The Centre is producing bi-weekly reports with projections and observations for each country. Code repository, including all the source code scripts necessary to run the model. leonardo.milano@un.org
Imperial College London Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul Latif Jameel Institute for Disease and Emergency Analytics Affected people Pilot : Great Britain Global coverage Stochastic individual-based simulation model that uses population movements between households, schools, workplaces, and the community to estimate transmission. Healthcare demand and the effects of non-pharmaceutical interventions are estimated. Key Results: Total number of infections; expected deaths; hospitalizations; scenario building 2 weeks https://github.com/mrc-ide/squire Patrick GT Walker Charles Whittaker Oliver Watson https://mrc-ide.github.io/global-lmic-reports/ https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-12-global-impact-covid-19/ https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf
Robert Verity study Imperial College London Affected people China A study of age-stratified data from mainland China that estimates duration from onset-of-symptoms to death or hospital discharge, and case fatality rate. Static Model https://github.com/mrc-ide/COVID19_CFR_submission Robert Verity: r.verity@imperial.ac.uk Lucy C Okell: l.okell@imperial.ac.uk Ilaria Dorigatti: i.dorigatti@imperial.ac.uk https://www.medrxiv.org/content/10.1101/2020.03.09.20033357v1.full.pdf+html
COVID Act Now Georgetown University Center for Global Health Science and Security, Stanford University Clinical Excellence Research Center, Grand Rounds Affected people United States Open source model based on SEIR model used to provide state-specific projections of infection rates and ICU bed usage. Subnational projections for USA are reported. Dashboards for data visualization and APIs to explore all datasets available. Key Results: Total number of infections; expected deaths; hospitalizations; scenario building Data and estimates updated daily https://github.com/covid-projections/covid-projections info@covidactnow.org. https://www.covidactnow.org/ References: https://docs.google.com/document/d/1ETeXAfYOvArfLvlxExE0_xrO5M4ITC0_Am38CRusCko/preview# Data Sources: https://docs.google.com/presentation/d/1XmKCBWYZr9VQKFAdWh_D7pkpGGM_oR9cPjj-UrNdMJQ/edit#slide=id.g875b45be96_0_301
COVID-19 Projections Institute for Health Metrics and Evaluation Affected people Argentina; Belgium; Bolivia; Brazil; Bulgaria: Canada; Chile; Colombia; Croatia; Cuba; Cyprus; Czechia; Denmark; Dominican Republic; Ecuador; Egypt; Estonia; Finland; Germany; Greece; Honduras; Hungary; Iceland; Ireland; Israel; Italy; Japan ; Latvia; Lithuania; Luxembourg; Malaysia; Mexico; Netherlands; Norway; Panama; Peru; Puerto Rico ; S. Korea; Moldova; Romania; Russia; Serbia; Slovenia; Spain; Sweden; Turkey; Ukraine; United Kingdom; USA Mixed effects nonlinear regression framework is employed by the authors to estimate the trajectory of cumulative and daily death rate. Additional model parameters such as implementation of social distancing measures are included. The study is supported by additional evidence from mobile phone data. Additional simulations to project hospital admissions, ICU admissions, length of stay, and ventilator need are included. Key Results: Total number of infections; expected deaths; hospitalizations; scenario building Revised estimates released every 2-3 days http://www.healthdata.org/covid/faqs#differences%20in%20modeling media@healthdata.org https://covid19.healthdata.org/united-states-of-america http://www.healthdata.org/covid/publications https://www.medrxiv.org/content/10.1101/2020.04.21.20074732v1.article-metrics
Multi-Model Outbreak Decision Support (MMODS) MIDAS Coordination Center Penn State University Affected people Global This is a decision theoretic framework that takes in results from multiple individual models to produce a unified set of projections and recommendations. By taking into account multiple modelling efforts decision makers can avoid cognitive biases, and insights from many different modeling approaches can be included. Static Model https://science.sciencemag.org/content/368/6491/577 Katriona Shea - k-shea@psu.edu https://midasnetwork.us/mmods/
Reich Lab COVID-19 Forecast Hub MIDAS Coordination Center University of Massachusetts at Amherst Affected people United States The ensemble forecast dashboard combines multiple models; unconditional on particular interventions being in place with those conditional on certain social distancing measures continuing. 21+ different models with 4 week-ahead forecasts ahead are included in the ensemble and their weekly predictions are visualized on the dashboard. Data and estimates updated daily https://github.com/reichlab/covid19-forecast-hub Dr. Nick Reich - nick@schoolph.umass.edu https://reichlab.io/covid19-forecast-hub/
Nowcasting model for real time epidemic tracking MIDAS Coordination Center Harvard T.H. Chan School of Public Health Centers for Disease Control and Prevention Affected people Global Nowcasting model here attempts to estimate the complete case counts for a given reporting date, using a time series of case reporting that is known to be incomplete due to reporting delays. The model uses Bayesian Smoothing, accounts for serial auto correlation and gives output estimates that are smooth and accurate in multiple disease settings. Depending on input time series https://github.com/sarahhbellum/NobBS Sarah F. McGough - sfm341@mail.harvard.edu Nicolas A. Menzies - nmenzies@hsph.harvard.edu https://www.biorxiv.org/content/10.1101/663823v1.full
Stochastic model for the transmission of 2019-nCov in Wuhan Center for the Ecology of Infectious Diseases, University of Georgia Affected people Wuhan, China Stochastic SEIR based model that includes additional time-varying rates of case detection, patient isolation, and case notification parameters. By using forward simulation, the model enables the generation of predictions about the future trajectory of the epidemic under alternative scenarios for containment. The model parameters have been calibrated using multiple different input sources. Key Results: Total number of infections; scenario building Static Model https://github.com/CEIDatUGA/ncov-wuhan-stochastic-model https://www.covid19.uga.edu/stochastic-model.html John M. Drake Pejman Rohani https://www.covid19.uga.edu/stochastic.html
Scenario analysis for the transmission of COVID‑19 Center for the Ecology of Infectious Diseases, University of Georgia Affected people Georgia,United States Stochastic SEIR based model which is employed for location- Georgia USA for scenario building under various interventions such as increased social distancing, increased hygiene, against returning to normal. Key Results: Total number of infections; expected deaths; hospitalizations; scenario building Data and estimates updated daily https://www.covid19.uga.edu/stochastic-GA.html https://github.com/CEIDatUGA/COVID-GA-model John M. Drake Andreas Handel https://www.covid19.uga.edu/stochastic-fitting-georgia-suplement.html
Gravity model for spread of 2019-nCov in China Center for the Ecology of Infectious Diseases, University of Georgia Affected people China Gravity-based model the risk of spatial spread of the 2019-nCov at the admin2 (prefecture) level in China, and to determine the efficacy of quarantines and travel restrictions imposed in Wuhan and other prefectures. Static Model https://github.com/CEIDatUGA/CoronavirusSpatial John M. Drake https://www.covid19.uga.edu/spatial-china.html
Probability of Widespread Transmission Center for the Ecology of Infectious Diseases, University of Georgia Affected people NA The model attempts to project the potential size of the COVID-19 outbreak by repurposing a previously developed Ebola model. This model can provide global ballpark figures of worse, worst, average case scenarios. Ongoing work on this model includes parametrization to have more region specific insight. Key Results: Total number of infections; scenario building Static Model https://github.com/CEIDatUGA/ncov-coupled-outbreaks Andrew W. Park John M. Drake https://www.covid19.uga.edu/final-size-supplement.html https://link.springer.com/chapter/10.1007/978-3-319-40413-4_3
Mass Testing and interventions impact Center for the Ecology of Infectious Diseases, University of Georgia Affected people China This model assesses the impact of a symptom-based mass screening and testing intervention during a novel infectious disease outbreak in China. Static Model https://www.covid19.uga.edu/mass_testing.html Yang Ge - ygechn@gmail.com Brian McKay Shengzhi Sun https://www.medrxiv.org/content/10.1101/2020.02.20.20025973v2
Nowcasting the size of COVID 19 Center for the Ecology of Infectious Diseases, University of Georgia Affected people United States This model aims to nowcast the number of latent and symptomatic infections from a point in the past forward to the present. The authors accomplish this by fitting a time varying autoregressive model, and then forecast the fitted model curve using an exponential smoothing (ETS) model. The model works to determine the ratio of detected cases to the true number of cases i.e the ascertainment rate. Key Results: Total number of infections Data and estimates updated daily https://github.com/CEIDatUGA/ncov-nowcast John M. Drake https://www.covid19.uga.edu/nowcast.html
SARS-CoV-2 mortality during the early stages of an epidemic Institute of Social and Preventive Medicine, University of Bern Columbia University Imperial College London Affected people Hubei, China; Austria; Baden-Württemberg (Germany); Lombardy (Italy); Spain; Switzerland This study attempts to simulate the transmission dynamics of SARS-CoV-2 using publicly available surveillance data. The model is then used to infer estimates of SARS-CoV-2 mortality, adjusted for biases. It also examines the case fatality ratio (CFR), the symptomatic case fatality ratio (sCFR) and the infection fatality ratio (IFR) in different geographic locations. Key Results: Total number of infections Static Model https://github.com/jriou/covid_adjusted_cfr Anthony Hause Michel J. Counotte Charles C. Margossian https://www.medrxiv.org/content/10.1101/2020.03.04.20031104v1.full.pdf https://statmodeling.stat.columbia.edu/2020/03/09/coronavirus-model-update-background-assumptions-and-room-for-improvement/ https://statmodeling.stat.columbia.edu/2020/03/07/coronavirus-age-specific-fatality-ratio-estimated-using-stan/
Estimating unobserved SARS-CoV-2 infections in the United States University of Notre Dame Affected people United States This model aims to estimate the extent of community transmission of SARS-CoV-2 in the US that occurred prior to its widespread recognition. The extent of community transmission of SARS-CoV-2 is estimated using a stochastic simulation model that combined importation and local transmission processes. Static Model Alex Perkins - taperkins@nd.edu Sean M. Cavany - scavany@nd.edu https://www.medrxiv.org/content/10.1101/2020.03.15.20036582v2
Real-time forecasting of infectious disease dynamics London School of Hygiene & Tropical Medicine Affected people NA A Bayesian semi-mechanistic SEIR model of infectious diseases, that was used in real time during the 2013–2016 West African Ebola epidemic is presented. This model can be used for real-time forecasting of infectious diseases. Specific SARS-CoV-2 projections are not a part of this model at this stage. Static Model https://github.com/sbfnk/ebola_forecasting_challenge Sebastian Funks - ebastian.funk@lshtm.ac.uk https://www.sciencedirect.com/science/article/pii/S1755436516300445
Effect of travel restrictions on the spread of the (COVID-19) outbreak Northeastern University, Bruno Kessler Foundation, ISI Foundation Affected people Global The authors use a global metapopulation disease transmission model (GLEAM) to project the impact of travel limitations on the national and international spread of the epidemic. The model is calibrated on the basis of internationally reported cases. The outputs are multiple dashboards which track the number of cases and risk of virus importation Data and estimates updated daily https://www.mobs-lab.org/2019ncov.html https://github.com/mobs-lab/COVID-19/blob/master/README.md Matteo Chinazzi Jessica T. Davis Marco Ajelli Corrado Gioannini https://science.sciencemag.org/content/368/6489/395 https://www.mobs-lab.org/2019ncov.html https://datastudio.google.com/u/0/reporting/3ffd36c3-0272-4510-a140-39e288a9f15c/page/U5lCB http://www.gleamviz.org/
Machine Learning the Phenomenology of COVID-19 Rensselaer Polytechnic Institute Affected people United States Data-driven machine learning analysis of the COVID-19 pandemic from its early infection dynamics, specifically infection counts over time. The goal of this model is to project the infectious force i.e the rate of a mild infection becoming serious. Additionally the model estimates asymptomatic infections and makes overall predictions of new infections over time Static Model Malik Magdon-Ismail - magdon@cs.rpi.edu https://arxiv.org/pdf/2003.07602.pdf https://www.medrxiv.org/content/10.1101/2020.03.17.20037309v3
CORVID: Modeling layered non-pharmaceutical interventions against SARS-CoV-2 Institute for Disease Modeling, Patient Knowhow Inc. Affected people United States Covid is an individual-based model that simulates the spread of SARS-CoV-2 in synthetic populations that represent communities in the United States.This model can be used to understand the potential effectiveness of non-pharmaceutical interventions (NPIs) for both suppression and mitigation efforts. Static Model https://github.com/dlchao/corvid Dennis Chao Assaf P Oron Devabhaktuni Srikrishna https://www.medrxiv.org/content/10.1101/2020.04.08.20058487v1
CHIME (COVID-19 Hospital Impact Model for Epidemics) Penn Medicine, University of Pennsylvania Affected people United States The CHIME model and web app is designed to assist hospitals and public health officials with understanding hospital capacity in response to the COVID pandemic. The model outputs provide estimates of total daily and running totals of inpatient hospitalizations, ICU admissions, and patients requiring ventilation. The model is built using a SIR (Susceptible, Infected, Recovered) epidemiological model. Key Results: Total number of infections; expected deaths; hospitalizations; scenario building Data and estimates updated daily https://github.com/CodeForPhilly/chime Michael Becker Corey Chivers pennsignals@uphs.upenn.edu https://penn-chime.phl.io/ http://predictivehealthcare.pennmedicine.org/2020/03/14/accouncing-chime.html
CoMo Consortium - COVID-19 Web App Covid-19 International Modelling Consortium (CoMo Consortium) Affected people Global The CoMo model is a simulation based tool designed to emulate multiple scenarios,impacts of non pharmacological interventions, to make reliable estimates and predictions about the spread, hospitalizations and deaths related to COVID-19. There are a range of input parameter values to customize simulations. Although explicit modeling parameters have not been made public it is being used extensively by decision makers where explicit country based models do not exist. Key Results: Total number of infections; expected deaths; hospitalizations; scenario building 2 weeks https://github.com/ocelhay/como Olivier Celhay Ricardo Aguas Sai Thein Than Tun Lisa J. White https://comomodel.net/
EPIDEMIC FORECASTING: COVID-19 Future of Humanity Institute, University of Oxford Total number of infections; scenario building Global The epidemic forecasting model and online tool can be used to calculate the estimated effect of various combinations of COVID countermeasures. Multiple parameters and compliance rates can be adjusted by decision makers. The output of the model is effective reporduction number which is the expected number of infections directly generated by one additional infection. This model works in conjunction with the GLEAM model (also in the catalogue) Key Results: Total number of infections; scenario building Data and estimates updated daily Jan Kulveit (jan.kulveit@philosophy.ox.ac.uk) http://epidemicforecasting.org/models