Publications by authors named "Michael Johansson"

Leptospirosis, an acute bacterial zoonotic disease, is endemic in Puerto Rico. Infection in approximately 10%-15% of patients with clinical disease progresses to severe, potentially fatal illness. Increased incidence has been associated with flooding in endemic areas around the world.

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The COVID-19 pandemic has highlighted the need to upgrade systems for infectious disease surveillance and forecasting and modeling of the spread of infection, both of which inform evidence-based public health guidance and policies. Here, we discuss requirements for an effective surveillance system to support decision making during a pandemic, drawing on the lessons of COVID-19 in the U.S.

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During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org).

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Dengue viruses (DENV) are endemic in the US territories of Puerto Rico, American Samoa, and the US Virgin Islands, with focal outbreaks also reported in the states of Florida and Hawaii. However, little is known about the intensity of dengue virus transmission over time and how dengue viruses have shaped the level of immunity in these populations, despite the importance of understanding how and why levels of immunity against dengue may change over time. These changes need to be considered when responding to future outbreaks and enacting dengue management strategies, such as guiding vaccine deployment.

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During May 2022-April 2023, dengue virus serotype 3 was identified among 601 travel-associated and 61 locally acquired dengue cases in Florida, USA. All 203 sequenced genomes belonged to the same genotype III lineage and revealed potential transmission chains in which most locally acquired cases occurred shortly after introduction, with little sustained transmission.

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Human movement is increasingly being recognized as a major driver of arbovirus risk and dissemination. The Communities Organized to Prevent Arboviruses (COPA) study is a cohort in southern Puerto Rico to measure arboviral prevalence, evaluate interventions, and collect mobility data. To quantify the relationship between arboviral prevalence and human mobility patterns, we fit multilevel logistic regression models to estimate odds ratios for mobility-related predictors of positive chikungunya IgG or Zika IgM test results collected from COPA, assuming mobility data does not change substantially from year to year.

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Article Synopsis
  • West Nile virus (WNV) is the main cause of mosquito-borne diseases in the continental U.S., but predicting its spread is complicated due to varying factors like environment and ecology.
  • Researchers developed 10 different models, including machine learning techniques, to predict annual cases of WNV neuroinvasive disease (WNND) from 2015 to 2021 across different climate regions.
  • The study found that historical WNND cases and population density were key predictors, and while some machine learning models showed promise, none outperformed simpler models based on historical data.
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Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.

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Article Synopsis
  • The text serves as a correction for a previously published article, identified by the DOI 10.1371/journal.pmed.1003793.
  • It indicates that there were errors or inaccuracies in the original publication that needed to be addressed.
  • The correction ensures the integrity and accuracy of the scientific record for readers and researchers.
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Background: Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used.

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Article Synopsis
  • The COVID-19 Scenario Modeling Hub, starting in December 2020, provided data-driven projections on cases, hospitalizations, and deaths resulting from COVID-19, pulling insights from up to nine different modeling groups.
  • These projections were crucial for understanding the effects of various interventions and informed the public and health authorities, including the CDC, about potential future trends and vaccination strategies.
  • The hub's collaborative approach allowed for rapid sharing of information amid uncertainties, highlighting how these models directly influenced public health communication and decision-making, while also outlining challenges faced during this process.
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Our ability to forecast epidemics more than a few weeks into the future is constrained by the complexity of disease systems, our limited ability to measure the current state of an epidemic, and uncertainties in how human action will affect transmission. Realistic longer-term projections (spanning more than a few weeks) may, however, be possible under defined scenarios that specify the future state of critical epidemic drivers, with the additional benefit that such scenarios can be used to anticipate the comparative effect of control measures. Since December 2020, the U.

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Article Synopsis
  • Policymakers face challenges in making decisions with limited information and conflicting predictions from different models, especially during crises like the COVID-19 pandemic.
  • A study brought together multiple modeling teams to assess reopening strategies in a mid-sized U.S. county, revealing consistent rankings for interventions despite variations in projection magnitudes.
  • The findings indicated that reopening workplaces could lead to a significant increase in infections, while restrictions could greatly reduce cumulative infections, highlighting the trade-offs between public health and economic activity with no optimal reopening strategy identified.
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Reconstructing the incidence of SARS-CoV-2 infection is central to understanding the state of the pandemic. Seroprevalence studies are often used to assess cumulative infections as they can identify asymptomatic infection. Since July 2020, commercial laboratories have conducted nationwide serosurveys for the U.

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Background: West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale.

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Article Synopsis
  • The COVID-19 Scenario Modeling Hub brought together nine teams to analyze the effects of vaccinating children aged 5-11 against SARS-CoV-2 on COVID-19 outcomes in the U.S. from September 2021 to March 2022.
  • The study compared outcomes under scenarios with and without vaccination and the potential emergence of more transmissible variants, providing insights on case counts, hospitalizations, and deaths.
  • Findings indicated that vaccinating children could significantly reduce COVID-19 cases, hospitalizations, and deaths, offering both direct benefits for kids and indirect benefits for the broader population, even in scenarios with more transmissible variants.
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The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden of COVID-19 in the United States from many contributing teams.

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In Spring 2021, the highly transmissible SARS-CoV-2 Delta variant began to cause increases in cases, hospitalizations, and deaths in parts of the United States. At the time, with slowed vaccination uptake, this novel variant was expected to increase the risk of pandemic resurgence in the US in summer and fall 2021. As part of the COVID-19 Scenario Modeling Hub, an ensemble of nine mechanistic models produced 6-month scenario projections for July-December 2021 for the United States.

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Article Synopsis
  • The study investigates how COVID-19 mitigation measures affected the spread of seasonal respiratory viruses in sub-tropical climates, specifically in southern Puerto Rico.
  • By comparing data from the 2019-2020 respiratory season with previous seasons (2012-2018), researchers found significant decreases in the test-positivity rates of several viruses, including Influenza A and B, RSV, and adenovirus.
  • The findings suggest that the stay-at-home orders and social distancing during the pandemic may have led to a reduced transmission of these respiratory viruses.
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Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.

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Background: SARS-CoV-2 vaccination of persons aged 12 years and older has reduced disease burden in the United States. The COVID-19 Scenario Modeling Hub convened multiple modeling teams in September 2021 to project the impact of expanding vaccine administration to children 5-11 years old on anticipated COVID-19 burden and resilience against variant strains.

Methods: Nine modeling teams contributed state- and national-level projections for weekly counts of cases, hospitalizations, and deaths in the United States for the period September 12, 2021 to March 12, 2022.

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The Advisory Committee on Immunization Practices (ACIP) recommended phased allocation of SARS-CoV-2 vaccines in December 2020. To support the development of this guidance, we used a mathematical model of SARS-CoV-2 transmission to evaluate the relative impact of three vaccine allocation strategies on infections, hospitalizations, and deaths. All three strategies initially prioritized healthcare personnel (HCP) for vaccination.

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Background: The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research.

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Article Synopsis
  • Probabilistic forecasts are crucial for understanding how newly emerged pathogens, like Zika, spread, but uncertainties about these pathogens complicate model selection.
  • A study evaluated 16 different forecasting models during the 2015-2016 Zika epidemic in Colombia, each with unique assumptions regarding human mobility and virus introduction.
  • Results indicated that model effectiveness varied over time, with some individual models doing better early on, but overall, ensemble models that considered multiple assumptions provided more reliable forecasts.
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