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  1. Article ; Online: Limitations of using mobile phone data to model COVID-19 transmission in the USA.

    Badr, Hamada S / Gardner, Lauren M

    The Lancet. Infectious diseases

    2020  Volume 21, Issue 5, Page(s) e113

    MeSH term(s) COVID-19 ; Cell Phone ; Humans ; SARS-CoV-2 ; United States/epidemiology
    Keywords covid19
    Language English
    Publishing date 2020-11-02
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 2061641-7
    ISSN 1474-4457 ; 1473-3099
    ISSN (online) 1474-4457
    ISSN 1473-3099
    DOI 10.1016/S1473-3099(20)30861-6
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  2. Article ; Online: Incorporating variant frequencies data into short-term forecasting for COVID-19 cases and deaths in the USA: a deep learning approach.

    Du, Hongru / Dong, Ensheng / Badr, Hamada S / Petrone, Mary E / Grubaugh, Nathan D / Gardner, Lauren M

    EBioMedicine

    2023  Volume 89, Page(s) 104482

    Abstract: Background: Since the US reported its first COVID-19 case on January 21, 2020, the science community has been applying various techniques to forecast incident cases and deaths. To date, providing an accurate and robust forecast at a high spatial ... ...

    Abstract Background: Since the US reported its first COVID-19 case on January 21, 2020, the science community has been applying various techniques to forecast incident cases and deaths. To date, providing an accurate and robust forecast at a high spatial resolution has proved challenging, even in the short term.
    Method: Here we present a novel multi-stage deep learning model to forecast the number of COVID-19 cases and deaths for each US state at a weekly level for a forecast horizon of 1-4 weeks. The model is heavily data driven, and relies on epidemiological, mobility, survey, climate, demographic, and SARS-CoV-2 variant frequencies data. We implement a rigorous and robust evaluation of our model-specifically we report on weekly performance over a one-year period based on multiple error metrics, and explicitly assess how our model performance varies over space, chronological time, and different outbreak phases.
    Findings: The proposed model is shown to consistently outperform the CDC ensemble model for all evaluation metrics in multiple spatiotemporal settings, especially for the longer-term (3 and 4 weeks ahead) forecast horizon. Our case study also highlights the potential value of variant frequencies data for use in short-term forecasting to identify forthcoming surges driven by new variants.
    Interpretation: Based on our findings, the proposed forecasting framework improves upon the available state-of-the-art forecasting tools currently used to support public health decision making with respect to COVID-19 risk.
    Funding: This work was funded the NSF Rapid Response Research (RAPID) grant Award ID 2108526 and the CDC Contract #75D30120C09570.
    MeSH term(s) Humans ; United States ; COVID-19 ; SARS-CoV-2 ; Deep Learning ; Benchmarking ; Forecasting
    Language English
    Publishing date 2023-02-21
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2851331-9
    ISSN 2352-3964
    ISSN (online) 2352-3964
    DOI 10.1016/j.ebiom.2023.104482
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Evolving Drivers of Brazilian SARS-CoV-2 Transmission: A Spatiotemporally Disaggregated Time Series Analysis of Meteorology, Policy, and Human Mobility.

    Kerr, Gaige Hunter / Badr, Hamada S / Barbieri, Alisson F / Colston, Josh M / Gardner, Lauren M / Kosek, Margaret N / Zaitchik, Benjamin F

    GeoHealth

    2023  Volume 7, Issue 3, Page(s) e2022GH000727

    Abstract: Brazil has been severely affected by the COVID-19 pandemic. Temperature and humidity have been purported as drivers of SARS-CoV-2 transmission, but no consensus has been reached in the literature regarding the relative roles of meteorology, governmental ... ...

    Abstract Brazil has been severely affected by the COVID-19 pandemic. Temperature and humidity have been purported as drivers of SARS-CoV-2 transmission, but no consensus has been reached in the literature regarding the relative roles of meteorology, governmental policy, and mobility on transmission in Brazil. We compiled data on meteorology, governmental policy, and mobility in Brazil's 26 states and one federal district from June 2020 to August 2021. Associations between these variables and the time-varying reproductive number (
    Language English
    Publishing date 2023-03-21
    Publishing country United States
    Document type Journal Article
    ISSN 2471-1403
    ISSN (online) 2471-1403
    DOI 10.1029/2022GH000727
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: The Planetary Child Health & Enterics Observatory (Plan-EO): A protocol for an interdisciplinary research initiative and web-based dashboard for mapping enteric infectious diseases and their risk factors and interventions in LMICs.

    Colston, Josh M / Fang, Bin / Houpt, Eric / Chernyavskiy, Pavel / Swarup, Samarth / Gardner, Lauren M / Nong, Malena K / Badr, Hamada S / Zaitchik, Benjamin F / Lakshmi, Venkataraman / Kosek, Margaret N

    PloS one

    2024  Volume 19, Issue 2, Page(s) e0297775

    Abstract: Background: Diarrhea remains a leading cause of childhood illness throughout the world that is increasing due to climate change and is caused by various species of ecologically sensitive pathogens. The emerging Planetary Health movement emphasizes the ... ...

    Abstract Background: Diarrhea remains a leading cause of childhood illness throughout the world that is increasing due to climate change and is caused by various species of ecologically sensitive pathogens. The emerging Planetary Health movement emphasizes the interdependence of human health with natural systems, and much of its focus has been on infectious diseases and their interactions with environmental and human processes. Meanwhile, the era of big data has engendered a public appetite for interactive web-based dashboards for infectious diseases. However, enteric infectious diseases have been largely overlooked by these developments.
    Methods: The Planetary Child Health & Enterics Observatory (Plan-EO) is a new initiative that builds on existing partnerships between epidemiologists, climatologists, bioinformaticians, and hydrologists as well as investigators in numerous low- and middle-income countries. Its objective is to provide the research and stakeholder community with an evidence base for the geographical targeting of enteropathogen-specific child health interventions such as novel vaccines. The initiative will produce, curate, and disseminate spatial data products relating to the distribution of enteric pathogens and their environmental and sociodemographic determinants.
    Discussion: As climate change accelerates there is an urgent need for etiology-specific estimates of diarrheal disease burden at high spatiotemporal resolution. Plan-EO aims to address key challenges and knowledge gaps by making and disseminating rigorously obtained, generalizable disease burden estimates. Pre-processed environmental and EO-derived spatial data products will be housed, continually updated, and made publicly available for download to the research and stakeholder communities. These can then be used as inputs to identify and target priority populations living in transmission hotspots and for decision-making, scenario-planning, and disease burden projection.
    Study registration: PROSPERO protocol #CRD42023384709.
    MeSH term(s) Child ; Humans ; Developing Countries ; Interdisciplinary Research ; Child Health ; Communicable Diseases/epidemiology ; Risk Factors ; Diarrhea/epidemiology ; Internet
    Language English
    Publishing date 2024-02-27
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0297775
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  6. Article: Associations between meteorology and COVID-19 in early studies: Inconsistencies, uncertainties, and recommendations.

    Kerr, Gaige Hunter / Badr, Hamada S / Gardner, Lauren M / Perez-Saez, Javier / Zaitchik, Benjamin F

    One health (Amsterdam, Netherlands)

    2021  Volume 12, Page(s) 100225

    Abstract: Meteorological variables, such as the ambient temperature and humidity, play a well-established role in the seasonal transmission of respiratory viruses and influenza in temperate climates. Since the onset of the novel coronavirus disease 2019 (COVID-19) ...

    Abstract Meteorological variables, such as the ambient temperature and humidity, play a well-established role in the seasonal transmission of respiratory viruses and influenza in temperate climates. Since the onset of the novel coronavirus disease 2019 (COVID-19) pandemic, a growing body of literature has attempted to characterize the sensitivity of COVID-19 to meteorological factors and thus understand how changes in the weather and seasonality may impede COVID-19 transmission. Here we select a subset of this literature, summarize the diversity in these studies' scopes and methodologies, and show the lack of consensus in their conclusions on the roles of temperature, humidity, and other meteorological factors on COVID-19 transmission dynamics. We discuss how several aspects of studies' methodologies may challenge direct comparisons across studies and inflate the importance of meteorological factors on COVID-19 transmission. We further comment on outstanding challenges for this area of research and how future studies might overcome them by carefully considering robust modeling approaches, adjusting for mediating and covariate effects, and choosing appropriate scales of analysis.
    Language English
    Publishing date 2021-02-09
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2834831-X
    ISSN 2352-7714
    ISSN 2352-7714
    DOI 10.1016/j.onehlt.2021.100225
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  7. Article ; Online: A Deep Learning Approach to Forecast Short-Term COVID-19 Cases and Deaths in the US

    Du, Hongru / Dong, Ensheng / Badr, Hamada S. / Petrone, Mary / Grubaugh, Nathan / Gardner, Lauren Marie

    medRxiv

    Abstract: Since the US reported its first COVID-19 case on January 21, 2020, the science community has been applying various techniques to forecast incident cases and deaths. To date, providing an accurate and robust forecast at a high spatial resolution has ... ...

    Abstract Since the US reported its first COVID-19 case on January 21, 2020, the science community has been applying various techniques to forecast incident cases and deaths. To date, providing an accurate and robust forecast at a high spatial resolution has proved challenging, even in the short term. Here we present a novel multi-stage deep learning model to forecast the number of COVID-19 cases and deaths for each US state at a weekly level for a forecast horizon of 1 to 4 weeks. The model is heavily data driven, and relies on epidemiological, mobility, survey, climate, and demographic. We further present results from a case study that incorporates SARS-CoV-2 genomic data (i.e. variant cases) to demonstrate the value of incorporating variant cases data into model forecast tools. We implement a rigorous and robust evaluation of our model - specifically we report on weekly performance over a one-year period based on multiple error metrics, and explicitly assess how our model performance varies over space, chronological time, and different outbreak phases. The proposed model is shown to consistently outperform the CDC ensemble model for all evaluation metrics in multiple spatiotemporal settings, especially for the longer-term (3 and 4 weeks ahead) forecast horizon. Our case study also highlights the potential value of virus genomic data for use in short-term forecasting to identify forthcoming surges driven by new variants. Based on our findings, the proposed forecasting framework improves upon the available forecasting tools currently used to support public health decision making with respect to COVID-19 risk.
    Keywords covid19
    Language English
    Publishing date 2022-08-24
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2022.08.23.22279132
    Database COVID19

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  8. Article ; Online: Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study.

    Badr, Hamada S / Du, Hongru / Marshall, Maximilian / Dong, Ensheng / Squire, Marietta M / Gardner, Lauren M

    The Lancet. Infectious diseases

    2020  Volume 20, Issue 11, Page(s) 1247–1254

    Abstract: Background: Within 4 months of COVID-19 first being reported in the USA, it spread to every state and to more than 90% of all counties. During this period, the US COVID-19 response was highly decentralised, with stay-at-home directives issued by state ... ...

    Abstract Background: Within 4 months of COVID-19 first being reported in the USA, it spread to every state and to more than 90% of all counties. During this period, the US COVID-19 response was highly decentralised, with stay-at-home directives issued by state and local officials, subject to varying levels of enforcement. The absence of a centralised policy and timeline combined with the complex dynamics of human mobility and the variable intensity of local outbreaks makes assessing the effect of large-scale social distancing on COVID-19 transmission in the USA a challenge.
    Methods: We used daily mobility data derived from aggregated and anonymised cell (mobile) phone data, provided by Teralytics (Zürich, Switzerland) from Jan 1 to April 20, 2020, to capture real-time trends in movement patterns for each US county, and used these data to generate a social distancing metric. We used epidemiological data to compute the COVID-19 growth rate ratio for a given county on a given day. Using these metrics, we evaluated how social distancing, measured by the relative change in mobility, affected the rate of new infections in the 25 counties in the USA with the highest number of confirmed cases on April 16, 2020, by fitting a statistical model for each county.
    Findings: Our analysis revealed that mobility patterns are strongly correlated with decreased COVID-19 case growth rates for the most affected counties in the USA, with Pearson correlation coefficients above 0·7 for 20 of the 25 counties evaluated. Additionally, the effect of changes in mobility patterns, which dropped by 35-63% relative to the normal conditions, on COVID-19 transmission are not likely to be perceptible for 9-12 days, and potentially up to 3 weeks, which is consistent with the incubation time of severe acute respiratory syndrome coronavirus 2 plus additional time for reporting. We also show evidence that behavioural changes were already underway in many US counties days to weeks before state-level or local-level stay-at-home policies were implemented, implying that individuals anticipated public health directives where social distancing was adopted, despite a mixed political message.
    Interpretation: This study strongly supports a role of social distancing as an effective way to mitigate COVID-19 transmission in the USA. Until a COVID-19 vaccine is widely available, social distancing will remain one of the primary measures to combat disease spread, and these findings should serve to support more timely policy making around social distancing in the USA in the future.
    Funding: None.
    MeSH term(s) Betacoronavirus ; COVID-19 ; Coronavirus Infections/epidemiology ; Coronavirus Infections/prevention & control ; Coronavirus Infections/transmission ; Coronavirus Infections/virology ; Government Regulation ; Humans ; Models, Statistical ; Pandemics/prevention & control ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/prevention & control ; Pneumonia, Viral/transmission ; Pneumonia, Viral/virology ; Public Health ; Quarantine/methods ; SARS-CoV-2 ; United States/epidemiology
    Keywords covid19
    Language English
    Publishing date 2020-07-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2061641-7
    ISSN 1474-4457 ; 1473-3099
    ISSN (online) 1474-4457
    ISSN 1473-3099
    DOI 10.1016/S1473-3099(20)30553-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic.

    Badr, Hamada S / Zaitchik, Benjamin F / Kerr, Gaige H / Nguyen, Nhat-Lan H / Chen, Yen-Ting / Hinson, Patrick / Colston, Josh M / Kosek, Margaret N / Dong, Ensheng / Du, Hongru / Marshall, Maximilian / Nixon, Kristen / Mohegh, Arash / Goldberg, Daniel L / Anenberg, Susan C / Gardner, Lauren M

    Scientific data

    2023  Volume 10, Issue 1, Page(s) 367

    Abstract: An impressive number of COVID-19 data catalogs exist. However, none are fully optimized for data science applications. Inconsistent naming and data conventions, uneven quality control, and lack of alignment between disease data and potential predictors ... ...

    Abstract An impressive number of COVID-19 data catalogs exist. However, none are fully optimized for data science applications. Inconsistent naming and data conventions, uneven quality control, and lack of alignment between disease data and potential predictors pose barriers to robust modeling and analysis. To address this gap, we generated a unified dataset that integrates and implements quality checks of the data from numerous leading sources of COVID-19 epidemiological and environmental data. We use a globally consistent hierarchy of administrative units to facilitate analysis within and across countries. The dataset applies this unified hierarchy to align COVID-19 epidemiological data with a number of other data types relevant to understanding and predicting COVID-19 risk, including hydrometeorological data, air quality, information on COVID-19 control policies, vaccine data, and key demographic characteristics.
    MeSH term(s) Humans ; Air Pollution ; COVID-19/epidemiology ; Pandemics ; Environment
    Language English
    Publishing date 2023-06-07
    Publishing country England
    Document type Dataset ; Journal Article
    ZDB-ID 2775191-0
    ISSN 2052-4463 ; 2052-4463
    ISSN (online) 2052-4463
    ISSN 2052-4463
    DOI 10.1038/s41597-023-02276-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Association between mobility patterns and COVID-19 transmission in the USA

    Badr, Hamada S / Du, Hongru / Marshall, Maximilian / Dong, Ensheng / Squire, Marietta M / Gardner, Lauren M

    The Lancet Infectious Diseases

    a mathematical modelling study

    2020  Volume 20, Issue 11, Page(s) 1247–1254

    Keywords Infectious Diseases ; covid19
    Language English
    Publisher Elsevier BV
    Publishing country us
    Document type Article ; Online
    ZDB-ID 2061641-7
    ISSN 1474-4457 ; 1473-3099
    ISSN (online) 1474-4457
    ISSN 1473-3099
    DOI 10.1016/s1473-3099(20)30553-3
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