Biology subjects

Johansson, M. A.

Publications and source records attributed to Johansson, M. A..

4 recordsLinked to original sources

bioRxiv2018

Minimum Information for Reusable Arthropod Abundance Data (MIReAAD)

Introduction Introduction Discussion Conclusion References Arthropods play a dominant role in the dynamics of practically all natural and human-modified terrestrial ecosystems1-3, and have significant economic and health effects. For example, certain insects provide significant economic benefits (e.g. pollination) exceeding $57 billion a year to the United States alone4. Meanwhile, invasive insects cost an estimated $70 billion dollars per year globally5 and insect pests may reduce agricultural harvests by up to 16%, with an equal amount of further losses of harvested goods6. Particularly noteworthy is a subset of arthropods that are disease vectors, transmitting pathogens to and between animals as well as plan ...

ecology↗

bioRxiv2018

Forecasting seasonal influenza in the U.S.: A collaborative multi-year, multi-model assessment of forecast performance

Influenza infects an estimated 9 to 35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multi-institution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the US for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of 7 targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the US, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1, 2 and 3 weeks ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision-making.

epidemiology↗

bioRxiv2018

Downgrading disease transmission risk estimates using terminal importations

As emerging and re-emerging infectious diseases like dengue, Ebola, chikungunya, and Zika threaten new populations worldwide, officials scramble to assess local severity and transmissibility, with little to no epidemiological history to draw upon. Standard methods for assessing autochthonous (local) transmission risk make either indirect estimates based on ecological suitability or direct estimates only after local cases accumulate. However, an overlooked source of epidemiological data that can meaningfully inform risk assessments prior to outbreak emergence is the absence of transmission by imported cases. Here, we present a method for updating a priori ecological estimates of transmission risk using real-time importation data. We demonstrate our method using Zika importation and transmission data from Texas in 2016, a high-risk region in the southern United States. Our updated risk estimates are lower than previously reported, with only six counties in Texas likely to sustain a Zika epidemic, and consistent with the number of autochthonous cases detected in 2017. Importation events can thereby provide critical, early insight into local transmission risks as infectious diseases expand their global reach.

epidemiology↗

bioRxiv2017

Detecting Local Zika Virus Transmission In The Continental United States: A Comparison Of Surveillance Strategies

IntroductionThe 2015-2017 Zika virus (ZIKV) epidemic in the Americas has driven efforts to strengthen surveillance systems and to develop interventions, testing, and travel recommendations. In the continental U.S. and Hawaii, where limited transmission has been observed, detecting local transmission is a key public health objective. We assessed the effectiveness of three general surveillance strategies for this situation: testing all pregnant women twice during pregnancy, testing blood donations, and testing symptomatic people who seek medical care in an emergency department (ED).\n\nMethodsWe developed a simulation model for each surveillance strategy and simulated different transmission scenarios with varying population sizes and infection rates. We then calculated the probability of detecting transmission, the number of tests needed, and the number of false positive test results.\n\nResultsThe probability of detecting ZIKV transmission was highest for testing ED patients with Zika symptoms, followed by pregnant women and blood donors, in that order. The magnitude of the difference in probability of detection between strategies depended on the incidence of infection. Testing ED patients required fewer tests and resulted in fewer false positives than surveillance among pregnant women. The optimal strategy identified was to test ED patients with at least two Zika virus disease symptoms. This case definition resulted in a high probability of detection with relatively few tests and false positives.\n\nDiscussionIn the continental U.S. and Hawaii, where local ZIKV transmission is rare, optimizing the probability of detecting infections while minimizing resource usage is particularly important. Local surveillance strategies will be influenced by existing public health system infrastructure, but should also consider the effectiveness of different approaches. This analysis demonstrated differences across strategies and indicated that testing symptomatic ED patients is generally a more efficient strategy for detecting transmission than routine testing of pregnant women or blood donors.

epidemiology↗