Characterizing the initial diffusion pattern of pandemic (H1N1) 2009 using surveillance data

Is there a relationship between industrial clusters and the prevalence of COVID-19 in the provinces of Morocco?

The main objective of this paper is to verify if there is a relationship between industrial agglomeration and the prevalence of COVID-19 and its diffusion within and between the provinces of Morocco. To do so, we used spatial exploratory analysis and spatial econometrics to show that the preponderance of industrial activity in a province has a significant effect on the number of active COVID-19 cases in that province. On the other hand, we have shown that the spatial diffusion of this effect is not significant, which indicates the appropriateness of the lockdown implemented. We have also shown that age, socio-economic deficits and habitat conditions are not significant determinants in the onset and spread of the pandemic.

Spatial and temporal clustering of patients hospitalized with laboratory-confirmed influenza in the United States

BACKGROUND

Timing of influenza spread across the United States is dependent on factors including local and national travel patterns and climate. Local epidemic intensity may be influenced by social, economic and demographic patterns. Data are needed to better explain how local socioeconomic factors influence both the timing and intensity of influenza seasons to result in national patterns.

METHODS

To determine the spatial and temporal impacts of socioeconomics on influenza hospitalization burden and timing, we used population-based laboratory-confirmed influenza hospitalization surveillance data from the CDC-sponsored Influenza Hospitalization Surveillance Network (FluSurv-NET) at up to 14 sites from the 2009/2010 through 2013/2014 seasons (n = 35,493 hospitalizations). We used a spatial scan statistic and spatiotemporal wavelet analysis, to compare temporal patterns of influenza spread between counties and across the country.

RESULTS

There were 56 spatial clusters identified in the unadjusted scan statistic analysis using data from the 2010/2011 through the 2013/2014 seasons, with relative risks (RRs) ranging from 0.09 to 4.20. After adjustment for socioeconomic factors, there were five clusters identified with RRs ranging from 0.21 to 1.20. In the wavelet analysis, most sites were in phase synchrony with one another for most years, except for the H1N1 pandemic year (2009-2010), wherein most sites had differential epidemic timing from the referent site in Georgia.

CONCLUSIONS

Socioeconomic factors strongly impact local influenza hospitalization burden. Influenza phase synchrony varies by year and by socioeconomics, but is less influenced by socioeconomics than is disease burden.

Do Season and Environment Have a Role in the Incidence of Anterior Uveitis Attacks?

PURPOSE

To investigate the seasonal influence on the incidence of uveitis attacks.

METHODS

An ecological study was designed including 731 uveitis attacks in 594 patients diagnosed at the eye emergency of a hospital in Madrid between 2014 and 2017. The incidence of uveitis attacks, B27+, and presumed herpetic attacks were calculated, and their correlations with seasonal and environmental variables in the same timeframe were analyzed. The analyzed variables were precipitation, barometric pressure, temperature, humidity, wind speed, global solar radiation, ultraviolet radiation, air pollution components (particulate matter and polluting gases), and the incidence of influenza.

RESULTS

The incidence of attacks was significantly higher in the winter than in the autumn (p = .025). It showed a significant correlation to the number of rainy days per month (r = 0.612;p = .04), and the average wind speed (r = 0.469;p = .02) after Bonferroni correction.

CONCLUSION

Uveitis episodes happened more frequently under rainy and windy conditions. Most factors were not significantly correlated to attacks.

The epidemiological and public health research response to 2009 pandemic influenza A(H1N1): experiences from Hong Kong

In recent years, Hong Kong has invested in research infrastructure to appropriately respond to novel infectious disease epidemics. Research from Hong Kong made a strong contribution to the international response to the 2009 influenza A (H1N1) pandemic (pH1N1). Summarizing, describing, and reviewing Hong Kong’s response to the 2009 pandemic, this article aimed to identify key elements of a real‐time research response. A systematic search in PubMed and EMBASE for research into the infection dynamics and natural history, impact, or control of pH1N1 in Hong Kong. Eligible articles were analyzed according to their scope. Fifty‐five articles were included in the review. Transmissibility of pH1N1 was similar in Hong Kong to elsewhere, and only a small fraction of infections were associated with severe disease. School closures were effective in reducing pH1N1 transmission, oseltamivir was effective for treatment of severe cases while convalescent plasma therapy has the potential to mitigate future pandemics. There was a rapid and comprehensive research response to pH1N1 in Hong Kong, providing important information on the epidemiology of the novel virus with relevance internationally as well as locally. The scientific knowledge gained through these detailed studies of pH1N1 is now being used to revise and update pandemic plans. The experiences of the research response in Hong Kong could provide a template for the research response to future emerging and reemerging disease epidemics.

Differential effects of pandemic (H1N1) 2009 on remote and indigenous groups, Northern Territory, Australia, 2009

Pandemic (H1N1) 2009 influenza spread through the Northern Territory, Australia, during June-August 2009. We performed 2 cross-sectional serologic surveys on specimens from Northern Territory residents, with 445 specimens obtained prepandemic and 1,689 specimens postpandemic. Antibody titers were determined by hemagglutination inhibition against reference virus A/California/7/2009 on serum samples collected opportunistically from outpatients. All specimens had data for patients' gender, age, and address, with patients' indigenous status determined for 94.1%. Protective immunity (titer >40) was present in 7.6% (95% confidence interval [CI] 5.2%-10.1%) of prepandemic specimens and 19.5% (95% CI 17.6%-21.4%) of postpandemic specimens, giving a population-standardized attack rate of 14.9% (95% CI 11.0%-18.9%). Prepandemic proportion of immune persons was greater with increasing age but did not differ by other demographic characteristics. Postpandemic proportion of immune persons was greater in younger groups and around double in indigenous persons. Postpandemic proportion immune was geographically heterogeneous, particularly among remote-living and indigenous groups.

Reconstruction of epidemic curves for pandemic influenza A (H1N1) 2009 at city and sub-city levels

To better describe the epidemiology of influenza at local level, the time course of pandemic influenza A (H1N1) 2009 in the city of Hong Kong was reconstructed from notification data after decomposition procedure and time series analysis. GIS (geographic information system) methodology was incorporated for assessing spatial variation. Between May and September 2009, a total of 24415 cases were successfully geocoded, out of 25473 (95.8%) reports in the original dataset. The reconstructed epidemic curve was characterized by a small initial peak, a nadir followed by rapid rise to the ultimate plateau. The full course of the epidemic had lasted for about 6 months. Despite the small geographic area of only 1000 Km2, distinctive spatial variation was observed in the configuration of the curves across 6 geographic regions. With the relatively uniform physical and climatic environment within Hong Kong, the temporo-spatial variability of influenza spread could only be explained by the heterogeneous population structure and mobility patterns. Our study illustrated how an epidemic curve could be reconstructed using regularly collected surveillance data, which would be useful in informing intervention at local levels.