Suchergebnisse

Abstract
Seasonal influenza kills hundreds of thousands every year, with multiple constantly changing strains in circulation at any given time. A high mutation rate enables the influenza virus to evade recognition by the human immune system, including immunity acquired through past infection and vaccination. Here, we capture the genetic similarity of influenza strains and their evolutionary dynamics with genotype networks. We show that the genotype networks of influenza A (H3N2) hemagglutinin are characterized by heavy-tailed distributions of module sizes and connectivity indicative of critical behavior. We argue that (i) genotype networks are driven by mutation and host immunity to explore a subspace of networks predictable in structure and (ii) genotype networks provide an underlying structure necessary to capture the rich dynamics of multistrain epidemic models. In particular, inclusion of strain-transcending immunity in epidemic models is dependent upon the structure of an underlying genotype network. This interplay is consistent with self-organized criticality where the epidemic dynamics of influenza locates critical regions of its genotype network. We conclude that this interplay between disease dynamics and network structure might be key for future network analysis of pathogen evolution and realistic multistrain epidemic models.

BackgroundThe death of George Floyd while in police custody has resurfaced serious questions about police conduct that result in the deaths of unarmed persons.ObjectiveData-driven strategies that identify and prioritize the public's needs may engender a public health response to improve policing. We assessed how internet searches indicative of interest in police reform changed after Mr Floyd's death.MethodsWe monitored daily Google searches (per 10 million total searches) that included the terms "police" and "reform(s)" (eg, "reform the police," "best police reforms," etc) originating from the United States between January 1, 2010, through July 5, 2020. We also monitored searches containing the term "police" with "training," "union(s)," "militarization," or "immunity" as markers of interest in the corresponding reform topics.ResultsThe 41 days following Mr Floyd's death corresponded with the greatest number of police "reform(s)" searches ever recorded, with 1,350,000 total searches nationally. Searches increased significantly in all 50 states and Washington DC. By reform topic, nationally there were 1,220,000 total searches for "police" and "union(s)"; 820,000 for "training"; 360,000 for "immunity"; and 72,000 for "militarization." In terms of searches for all policy topics by state, 33 states searched the most for "training," 16 for "union(s)," and 2 for "immunity." States typically in the southeast had fewer queries related to any police reform topic than other states. States that had a greater percentage of votes for President Donald Trump during the 2016 election searched more often for police "union(s)" while states favoring Secretary Hillary Clinton searched more for police "training."ConclusionsThe United States is at a historical juncture, with record interest in topics related to police reform with variability in search terms across states. Policy makers can respond to searches by considering the policies their constituencies are searching for online, notably police training and unions. Public health leaders can respond by engaging in the subject of policing and advocating for evidence-based policy reforms.

BackgroundThe death of George Floyd while in police custody has resurfaced serious questions about police conduct that result in the deaths of unarmed persons.ObjectiveData-driven strategies that identify and prioritize the public's needs may engender a public health response to improve policing. We assessed how internet searches indicative of interest in police reform changed after Mr Floyd's death.MethodsWe monitored daily Google searches (per 10 million total searches) that included the terms "police" and "reform(s)" (eg, "reform the police," "best police reforms," etc) originating from the United States between January 1, 2010, through July 5, 2020. We also monitored searches containing the term "police" with "training," "union(s)," "militarization," or "immunity" as markers of interest in the corresponding reform topics.ResultsThe 41 days following Mr Floyd's death corresponded with the greatest number of police "reform(s)" searches ever recorded, with 1,350,000 total searches nationally. Searches increased significantly in all 50 states and Washington DC. By reform topic, nationally there were 1,220,000 total searches for "police" and "union(s)"; 820,000 for "training"; 360,000 for "immunity"; and 72,000 for "militarization." In terms of searches for all policy topics by state, 33 states searched the most for "training," 16 for "union(s)," and 2 for "immunity." States typically in the southeast had fewer queries related to any police reform topic than other states. States that had a greater percentage of votes for President Donald Trump during the 2016 election searched more often for police "union(s)" while states favoring Secretary Hillary Clinton searched more for police "training."ConclusionsThe United States is at a historical juncture, with record interest in topics related to police reform with variability in search terms across states. Policy makers can respond to searches by considering the policies their constituencies are searching for online, notably police training and unions. Public health ...

NGR reports funding by NIGMS grant R35GM119582. BMA is supported by Bill and Melinda Gates Foundation through the Global Good Fund. SP and IMB were funded by the Armed Forces Health Surveillance Branch (GEIS: P0116_19_WR_03.11). ; Introduction: High quality epidemic forecasting and prediction are critical to support response to local, regional and global infectious disease threats. Other fields of biomedical research use consensus reporting guidelines to ensure standardization and quality of research practice among researchers, and to provide a framework for end-users to interpret the validity of study results. The purpose of this study was to determine whether guidelines exist specifically for epidemic forecast and prediction publications. Methods: We undertook a formal systematic review to identify and evaluate any published infectious disease epidemic forecasting and prediction reporting guidelines. This review leveraged a team of 18 investigators from US Government and academic sectors. Results: A literature database search through May 26, 2019, identified 1467 publications (MEDLINE n = 584, EMBASE n = 883), and a grey-literature review identified a further 407 publications, yielding a total 1777 unique publications. A paired-reviewer system screened in 25 potentially eligible publications, of which two were ultimately deemed eligible. A qualitative review of these two published reporting guidelines indicated that neither were specific for epidemic forecasting and prediction, although they described reporting items which may be relevant to epidemic forecasting and prediction studies. Conclusions: This systematic review confirms that no specific guidelines have been published to standardize the reporting of epidemic forecasting and prediction studies. These findings underscore the need to develop such reporting guidelines in order to improve the transparency, quality and implementation of epidemic forecasting and prediction research in operational public health. ; Publisher PDF ; Peer reviewed

INTRODUCTION: High quality epidemic forecasting and prediction are critical to support response to local, regional and global infectious disease threats. Other fields of biomedical research use consensus reporting guidelines to ensure standardization and quality of research practice among researchers, and to provide a framework for end-users to interpret the validity of study results. The purpose of this study was to determine whether guidelines exist specifically for epidemic forecast and prediction publications. METHODS: We undertook a formal systematic review to identify and evaluate any published infectious disease epidemic forecasting and prediction reporting guidelines. This review leveraged a team of 18 investigators from US Government and academic sectors. RESULTS: A literature database search through May 26, 2019, identified 1467 publications (MEDLINE n = 584, EMBASE n = 883), and a grey-literature review identified a further 407 publications, yielding a total 1777 unique publications. A paired-reviewer system screened in 25 potentially eligible publications, of which two were ultimately deemed eligible. A qualitative review of these two published reporting guidelines indicated that neither were specific for epidemic forecasting and prediction, although they described reporting items which may be relevant to epidemic forecasting and prediction studies. CONCLUSIONS: This systematic review confirms that no specific guidelines have been published to standardize the reporting of epidemic forecasting and prediction studies. These findings underscore the need to develop such reporting guidelines in order to improve the transparency, quality and implementation of epidemic forecasting and prediction research in operational public health.

INTRODUCTION: High quality epidemic forecasting and prediction are critical to support response to local, regional and global infectious disease threats. Other fields of biomedical research use consensus reporting guidelines to ensure standardization and quality of research practice among researchers, and to provide a framework for end-users to interpret the validity of study results. The purpose of this study was to determine whether guidelines exist specifically for epidemic forecast and prediction publications. METHODS: We undertook a formal systematic review to identify and evaluate any published infectious disease epidemic forecasting and prediction reporting guidelines. This review leveraged a team of 18 investigators from US Government and academic sectors. RESULTS: A literature database search through May 26, 2019, identified 1467 publications (MEDLINE n = 584, EMBASE n = 883), and a grey-literature review identified a further 407 publications, yielding a total 1777 unique publications. A paired-reviewer system screened in 25 potentially eligible publications, of which two were ultimately deemed eligible. A qualitative review of these two published reporting guidelines indicated that neither were specific for epidemic forecasting and prediction, although they described reporting items which may be relevant to epidemic forecasting and prediction studies. CONCLUSIONS: This systematic review confirms that no specific guidelines have been published to standardize the reporting of epidemic forecasting and prediction studies. These findings underscore the need to develop such reporting guidelines in order to improve the transparency, quality and implementation of epidemic forecasting and prediction research in operational public health.

Abstract
During outbreaks of emerging infectious diseases, internationally connected cities often experience large and early outbreaks, while rural regions follow after some delay. This hierarchical structure of disease spread is influenced primarily by the multiscale structure of human mobility. However, during the COVID-19 epidemic, public health responses typically did not take into consideration the explicit spatial structure of human mobility when designing nonpharmaceutical interventions (NPIs). NPIs were applied primarily at national or regional scales. Here, we use weekly anonymized and aggregated human mobility data and spatially highly resolved data on COVID-19 cases at the municipality level in Mexico to investigate how behavioral changes in response to the pandemic have altered the spatial scales of transmission and interventions during its first wave (March–June 2020). We find that the epidemic dynamics in Mexico were initially driven by exports of COVID-19 cases from Mexico State and Mexico City, where early outbreaks occurred. The mobility network shifted after the implementation of interventions in late March 2020, and the mobility network communities became more disjointed while epidemics in these communities became increasingly synchronized. Our results provide dynamic insights into how to use network science and epidemiological modeling to inform the spatial scale at which interventions are most impactful in mitigating the spread of COVID-19 and infectious diseases in general.