Influence of meteorological parameters in the seasonality of influenza viruses circulating in Northern Cameroon

Exploration of influenza incidence prediction model based on meteorological factors in Lanzhou, China, 2014-2017

Humans are susceptible to influenza. The influenza virus spreads quickly and behave seasonally. The seasonality and spread of influenza are often associated with meteorological factors and have spatio-temporal differences. Based on the influenza cases and daily average meteorological factors in Lanzhou from 2014 to 2017, this study firstly aimed to analyze the characteristics of influenza incidence in Lanzhou and the impact of meteorological factors on influenza activities. Then, SARIMA(X) models for the prediction were established. The influenza cases in Lanzhou from 2014 to 2017 was more male than female, and the younger the age, the higher the susceptibility; the epidemic characteristics showed that there is a peak in winter, a secondary peak in spring, and a trough in summer and autumn. The influenza cases in Lanzhou increased with increasing daily pressure, decreasing precipitation, average relative humidity, hours of sunshine, average daily temperature and average daily wind speed. Low temperature was a significant driving factor for the increase of transmission intensity of seasonal influenza. The SARIMAX (1,0,0)(1,0,1)[12] multivariable model with average temperature has better prediction performance than the university model. This model is helpful to establish an early warning system, and provide important evidence for the development of influenza control policies and public health interventions.

Short-term impact of ambient temperature on the incidence of influenza in Wuhan, China

Few studies have estimated the nonlinear association of ambient temperature with the risk of influenza. We therefore applied a time-series analysis to explore the short-term effect of ambient temperature on the incidence of influenza in Wuhan, China. Daily influenza cases were collected from Hubei Provincial Center for Disease Control and Prevention (Hubei CDC) from January 1, 2014, to December 31, 2017. The meteorological and daily pollutant data was obtained from the Hubei Meteorological Service Center and National Air Quality Monitoring Stations, respectively. We used a generalized additive model (GAM) coupled with the distributed lag nonlinear model (DLNM) to explore the exposure-lag-response relationship between the short-term risk of influenza and daily average ambient temperature. Analyses were also performed to assess the extreme cold and hot temperature effects. We observed that the ambient temperature was statistically significant, and the exposure-response curve is approximately S-shaped, with a peak observed at 23.57 ℃. The single-day lag curve showed that extreme hot and cold temperatures were both significantly associated with influenza. The extreme hot temperature has an acute effect on influenza, with the most significant effect observed at lag 0-1. The extreme cold temperature has a relatively smaller effect but lasts longer, with the effect exerted continuously during a lag of 2-4 days. Our study found significant nonlinear and delayed associations between ambient temperature and the incidence of influenza. Our finding contributes to the establishment of an early warning system for airborne infectious diseases.

The contrasting relationships of relative humidity with influenza A and B in a humid subtropical region

Influenza is an acute respiratory disease that seriously threatens public health. The occurrence of influenza has been proved to be related to a variety of meteorological factors. However, less attention has been paid to the effect of relative humidity (RH) on different types of influenza, especially in subtropical regions. Daily data on laboratory-confirmed influenza cases, weather variables, and air pollutants in Hefei covering the 2014-2019 period were collected. The seasonality and trend of daily influenza cases were explored by the time series seasonal decomposition method. Generalized linear model was fitted in conjunction with distributed lag nonlinear model to quantify the associations of RH with influenza A and influenza B. Subgroup analyses were conducted by sex, age (0-4, 5-17, and ≥18 years), and season (cold and warm seasons). A total of 5238 influenza cases including 2847 influenza A cases and 2391 influenza B cases were recorded. The epidemic of influenza presented a distinct seasonal pattern, and the number of daily influenza cases increased steadily since 2016. High RH was related to an increased risk of influenza A (maximum RR = 1.683, 95%CI: 1.365-2.076), especially among males, females, and school-age children. Low RH was associated with an increased risk of influenza B (maximum RR = 1.252, 95%CI: 1.169-1.340). The contrasting relationships of RH with influenza A and B remained significant in cold seasons. High RH and low RH were significantly associated with the increased risk of influenza A and B, respectively. The findings of our study may provide clues for proposing new effective interventions.

Forecasting influenza-like illness trends in Cameroon using Google Search Data

Although acute respiratory infections are a leading cause of mortality in sub-Saharan Africa, surveillance of diseases such as influenza is mostly neglected. Evaluating the usefulness of influenza-like illness (ILI) surveillance systems and developing approaches for forecasting future trends is important for pandemic preparedness. We applied and compared a range of robust statistical and machine learning models including random forest (RF) regression, support vector machines (SVM) regression, multivariable linear regression and ARIMA models to forecast 2012 to 2018 trends of reported ILI cases in Cameroon, using Google searches for influenza symptoms, treatments, natural or traditional remedies as well as, infectious diseases with a high burden (i.e., AIDS, malaria, tuberculosis). The R2 and RMSE (Root Mean Squared Error) were statistically similar across most of the methods, however, RF and SVM had the highest average R2 (0.78 and 0.88, respectively) for predicting ILI per 100,000 persons at the country level. This study demonstrates the need for developing contextualized approaches when using digital data for disease surveillance and the usefulness of search data for monitoring ILI in sub-Saharan African countries.

The epidemiology of seasonal influenza after the 2009 influenza pandemic in Africa: a systematic review

Background

Influenza infection is a serious public health problem that causes an estimated 3 to 5 million cases and 250,000 deaths worldwide every year. The epidemiology of influenza is well-documented in high- and middle-income countries, however minimal effort had been made to understand the epidemiology, burden and seasonality of influenza in Africa. This study aims to assess the state of knowledge of seasonal influenza epidemiology in Africa and identify potential data gaps for policy formulation following the 2009 pandemic.

Method

We reviewed articles from Africa published into four databases namely: MEDLINE (PubMed), Google Scholar, Cochrane Library and Scientific Research Publishing from 2010 to 2019.

Results

We screened titles and abstracts of 2070 studies of which 311 were selected for full content evaluation and 199 studies were considered. Selected articles varied substantially on the basis of the topics they addressed covering the field of influenza surveillance (n=80); influenza risk factors and co-morbidities (n=15); influenza burden (n=37); influenza vaccination (n=40); influenza and other respiratory pathogens (n=22) and influenza diagnosis (n=5).

Conclusion

Significant progress has been made since the last pandemic in understanding the influenza epidemiology in Africa. However, efforts still remain for most countries to have sufficient data to allow countries to prioritize strategies for influenza prevention and control.

Genetic diversity of influenza A(H3N2) viruses in Northern Cameroon during the 2014-2016 influenza seasons

In Cameroon, genome characterization of influenza virus has been performed only in the Southern regions meanwhile genetic diversity of this virus varies with respect to locality. The Northern region characterized by a Sudan tropical climate might have distinct genetic characterization. This study aimed to better understand the genetic diversity of influenza A(H3N2) viruses circulating in Northern Cameroon. Sequences of three gene segments (hemagglutinin (HA), neuraminidase (NA) and matrix (M) genes) were obtained from 16 A(H3N2) virus strains collected during the 2014 to 2016 influenza seasons in Garoua. The HA gene segments were analysed with respect to reference strains while the NA and M gene was analysed for reported genetic markers of resistance to antivirals. Analysis of the HA sequences revealed that majority of the virus strains grouped together with the 2016-2017 vaccine strain (3C.2a-A/Hong Kong/4801/2014) while 3/5 (60%) of the 2015 viral strains grouped together with the 2015-2016 vaccine strain 3C.3a-A/Switzerland/9715293/2013. Within clade 3C.2a, Northern Cameroon sequences mostly grouped in sub-clade A3 (10/16). Analysis of the coding regions of the NA and M genes showed that none had genetic markers of resistance to neuraminidase inhibitors but all strains possessed the S31N substitution of resistance to amantadine. Due to some discrepancies observed in this region with respect to the Southern regions of Cameroon, there is necessity of including all regions within a country in the sentinel surveillance of influenza. These data will enable to track changes in influenza viruses in Cameroon.

Respiratory Viral Infection: An Underappreciated Cause of Acute Febrile Illness Admissions in Southern Sri Lanka

The contribution of respiratory viruses to acute febrile illness (AFI) burden is poorly characterized. We describe the prevalence, seasonality, and clinical features of respiratory viral infection among AFI admissions in Sri Lanka. We enrolled AFI patients ≥ 1 year of age admitted to a tertiary care hospital in southern Sri Lanka, June 2012-October 2014. We collected epidemiologic/clinical data and a nasal or nasopharyngeal sample that was tested using polymerase chain reaction (Luminex NxTAG, Austin, TX). We determined associations between weather data and respiratory viral activity using the Spearman correlation and assessed respiratory virus seasonality using a Program for Appropriate Technology definition. Bivariable and multivariable regression analyses were conducted to identify features associated with respiratory virus detection. Among 964 patients, median age was 26.2 years (interquartile range 14.6-39.9) and 646 (67.0%) were male. One-fifth (203, 21.1%) had respiratory virus detected: 13.9% influenza, 1.4% human enterovirus/rhinovirus, 1.4% parainfluenza virus, 1.1% respiratory syncytial virus, and 1.1% human metapneumovirus. Patients with respiratory virus identified were younger (median 9.8 versus 27.7 years, P < 0.001) and more likely to have respiratory signs and symptoms. Influenza A and respiratory viral activity peaked in February-June each year. Maximum daily temperature was associated with influenza and respiratory viral activity (P = 0.03 each). Patients with respiratory virus were as likely as others to be prescribed antibiotics (55.2% versus 52.6%, P = 0.51), and none reported prior influenza vaccination. Respiratory viral infection was a common cause of AFI. Improved access to vaccines and respiratory diagnostics may help reduce disease burden and inappropriate antibiotic use.

Influence of meteorological parameters in the seasonality of influenza viruses circulating in Northern Cameroon

BACKGROUND

Several studies have demonstrated the role of meteorological parameters in the seasonality of influenza viruses in tropical and subtropical regions, most importantly temperature, humidity, and rainfall.

OBJECTIVES

This study aimed to describe the influence of meteorological parameters in the seasonality of influenza viruses in Northern Cameroon, a region characterized by high temperatures.

METHODS

This was a retrospective study performed in Garoua Cameroon from January 2014 to December 2016. Monthly proportions of confirmed influenza cases from six sentinel sites were considered as dependent variables, whereas monthly values of mean temperature, average relative humidity, and accumulated rainfall were considered as independent variables. A vector error correction model was used to determine the relationship between influenza activity and the meteorological variables.

RESULTS AND CONCLUSION

Analysis showed that there was a statistically significant association between overall influenza activity and influenza A activity with respect to average relative humidity. A unit increase in humidity within a given month leads to more than 85% rise in the overall influenza and influenza A activity 2 months later. Meanwhile, none of the three meteorological variables could explain influenza B activity. This observation is essential in filling the gap of knowledge and could help in the prevention and control strategies to strengthen influenza surveillance program in Cameroon.