Influenza-like illness in an urban community of Salvador, Brazil: incidence, seasonality and risk factors

A combination of annual and nonannual forces drive respiratory disease in the tropics

INTRODUCTION

It is well known that influenza and other respiratory viruses are wintertime-seasonal in temperate regions. However, respiratory disease seasonality in the tropics is less well understood. In this study, we aimed to characterise the seasonality of influenza-like illness (ILI) and influenza virus in Ho Chi Minh City, Vietnam.

METHODS

We monitored the daily number of ILI patients in 89 outpatient clinics from January 2010 to December 2019. We collected nasal swabs and tested for influenza from a subset of clinics from May 2012 to December 2019. We used spectral analysis to describe the periodic signals in the system. We evaluated the contribution of these periodic signals to predicting ILI and influenza patterns through lognormal and gamma hurdle models.

RESULTS

During 10 years of community surveillance, 66 799 ILI reports were collected covering 2.9 million patient visits; 2604 nasal swabs were collected, 559 of which were PCR-positive for influenza virus. Both annual and nonannual cycles were detected in the ILI time series, with the annual cycle showing 8.9% lower ILI activity (95% CI 8.8% to 9.0%) from February 24 to May 15. Nonannual cycles had substantial explanatory power for ILI trends (ΔAIC=183) compared with all annual covariates (ΔAIC=263) in lognormal regression. Near-annual signals were observed for PCR-confirmed influenza but were not consistent over time or across influenza (sub)types. The explanatory power of climate factors for ILI and influenza virus trends was weak.

CONCLUSION

Our study reveals a unique pattern of respiratory disease dynamics in a tropical setting influenced by both annual and nonannual drivers, with influenza dynamics showing near-annual periodicities. Timing of vaccination campaigns and hospital capacity planning may require a complex forecasting approach.

Spatiotemporal effects of meteorological conditions on global influenza peaks

BACKGROUND

Numerous studies have suggested that meteorological conditions such as temperature and absolute humidity are highly indicative of influenza outbreaks. However, the explanatory power of meteorological factors on the seasonal influenza peaks varied widely between countries at different latitudes.

OBJECTIVES

We aimed to explore the modification effects of meteorological factors on the seasonal influenza peaks in multi-countries.

METHODS

Data on influenza positive rate (IPR) were collected across 57 countries and data on meteorological factors were collected from ECMWF Reanalysis v5 (ERA5). We used linear regression and generalized additive models to investigate the spatiotemporal associations between meteorological conditions and influenza peaks in cold and warm seasons.

RESULTS

Influenza peaks were significantly correlated with months with both lower and higher temperatures. In temperate countries, the average intensity of cold season peaks was stronger than that of warm season peaks. However, the average intensity of warm season peaks was stronfger than of cold season peaks in tropical countries. Temperature and specific humidity had synergistic effects on influenza peaks at different latitudes, stronger in temperate countries (cold season: R2=0.90; warm season: R2=0.84) and weaker in tropical countries (cold season: R2=0.64; warm season: R2=0.03). Furthermore, the effects could be divided into cold-dry and warm-humid modes. The temperature transition threshold between the two modes was 16.5-19.5 °C. During the transition from cold-dry mode to warm-humid mode, the average 2 m specific humidity increased by 2.15 times, illustrating that transporting a large amount of water vapor may compensate for the negative effect of rising temperatures on the spread of the influenza virus.

CONCLUSION

Differences in the global influenza peaks were related to the synergistic influence of temperature and specific humidity. The global influenza peaks could be divided into cold-dry and warm-humid modes, and specific thresholds of meteorological conditions were needed for the transition of the two modes.

Spatial distribution and driving factors of the associations between temperature and influenza-like illness in the United States: a time-stratified case-crossover study

BACKGROUND

Several previous studies investigated the associations between temperature and influenza in a single city or region without a national picture. The attributable risk of influenza due to temperature and the corresponding driving factors were unclear. This study aimed to evaluate the spatial distribution characteristics of attributable risk of Influenza-like illness (ILI) caused by adverse temperatures and explore the related driving factors in the United States.

METHODS

ILI, meteorological factors, and PM_2.5 of 48 states in the United States were collected during 2011-2019. The time-stratified case-crossover design with a distributed lag non-linear model was carried out to evaluate the association between temperature and ILI at the state level. The multivariate meta-analysis was performed to obtain the combined effects at the national level. The attributable fraction (AF) was calculated to assess the ILI burden ascribed to adverse temperatures. The ordinary least square model (OLS), spatial lag model (SLM), and spatial error model (SEM) were utilized to identify driving factors.

RESULTS

A total of 7,716,115 ILI cases were included in this study. Overall, the temperature was negatively associated with ILI risk, and lower temperature gave rise to a higher risk of ILI. AF ascribed to adverse temperatures differed across states, from 49.44% (95% eCI: 36.47% ~ 58.68%) in Montana to 6.51% (95% eCI: -6.49% ~ 16.46%) in Wisconsin. At the national level, 29.08% (95% eCI: 27.60% ~ 30.24%) of ILI was attributable to cold. Per 10,000 dollars increase in per-capita income was associated with the increment in AF (OLS: β = -6.110, P = 0.021; SLM: β = -5.496, P = 0.022; SEM: β = -6.150, P = 0.022).

CONCLUSION

The cold could enhance the risk of ILI and result in a considerable proportion of ILI disease burden. The ILI burden attributed to cold varied across states and was higher in those states with lower economic status. Targeted prevention programs should be considered to lower the burden of influenza.

Transmission dynamics of seasonal influenza in a remote island population

Seasonal influenza outbreaks remain an important public health concern, causing large numbers of hospitalizations and deaths among high-risk groups. Understanding the dynamics of individual transmission is crucial to design effective control measures and ultimately reduce the burden caused by influenza outbreaks. In this study, we analyzed surveillance data from Kamigoto Island, Japan, a semi-isolated island population, to identify the drivers of influenza transmission during outbreaks. We used rapid influenza diagnostic test (RDT)-confirmed surveillance data from Kamigoto island, Japan and estimated age-specific influenza relative illness ratios (RIRs) over eight epidemic seasons (2010/11 to 2017/18). We reconstructed the probabilistic transmission trees (i.e., a network of who-infected-whom) using Bayesian inference with Markov-chain Monte Carlo method and then performed a negative binomial regression on the inferred transmission trees to identify the factors associated with onwards transmission risk. Pre-school and school-aged children were most at risk of getting infected with influenza, with RIRs values consistently above one. The maximal RIR values were 5.99 (95% CI 5.23, 6.78) in the 7-12 aged-group and 5.68 (95%CI 4.59, 6.99) in the 4-6 aged-group in 2011/12. The transmission tree reconstruction suggested that the number of imported cases were consistently higher in the most populated and busy districts (Tainoura-go and Arikawa-go) ranged from 10-20 to 30-36 imported cases per season. The number of secondary cases generated by each case were also higher in these districts, which had the highest individual reproduction number (R_eff: 1.2-1.7) across the seasons. Across all inferred transmission trees, the regression analysis showed that cases reported in districts with lower local vaccination coverage (incidence rate ratio IRR = 1.45 (95% CI 1.02, 2.05)) or higher number of inhabitants (IRR = 2.00 (95% CI 1.89, 2.12)) caused more secondary transmissions. Being younger than 18 years old (IRR = 1.38 (95%CI 1.21, 1.57) among 4-6 years old and 1.45 (95% CI 1.33, 1.59) 7-12 years old) and infection with influenza type A (type B IRR = 0.83 (95% CI 0.77, 0.90)) were also associated with higher numbers of onwards transmissions. However, conditional on being infected, we did not find any association between individual vaccination status and onwards transmissibility. Our study showed the importance of focusing public health efforts on achieving high vaccine coverage throughout the island, especially in more populated districts. The strong association between local vaccine coverage (including neighboring regions), and the risk of transmission indicate the importance of achieving homogeneously high vaccine coverage. The individual vaccine status may not prevent onwards transmission, though it may reduce the severity of infection.

Acute-Phase Levels of CXCL8 as Risk Factor for Chronic Arthralgia Following Chikungunya Virus Infection

The immunopathogenesis of chikungunya virus (CHIKV) infection and the role of acute-phase immune response on joint pain persistence is not fully understood. We investigated the profile of serum chemokine and cytokine in CHIKV-infected patients with acute disease, compared the levels of these biomarkers to those of patients with other acute febrile diseases (OAFD) and healthy controls (HC), and evaluated their role as predictors of chronic arthralgia development. Chemokines and cytokines were measured by flow Cytometric Bead Array. Patients with CHIKV infection were further categorized according to duration of arthralgia (≤ 3 months vs >3 months), presence of anti-CHIKV IgM at acute-phase sample, and number of days of symptoms at sample collection (1 vs 2-3 vs ≥4). Patients with acute CHIKV infection had significantly higher levels of CXCL8, CCL2, CXCL9, CCL5, CXCL10, IL-1β, IL-6, IL-12, and IL-10 as compared to HC. CCL2, CCL5, and CXCL10 levels were also significantly higher in patients with CHIKV infection compared to patients with OAFD. Patients whose arthralgia lasted > 3 months had increased CXCL8 levels compared to patients whose arthralgia did not (p<0.05). Multivariable analyses further indicated that high levels of CXCL8 and female sex were associated with arthralgia lasting >3 months. Patients with chikungunya and OAFD had similar cytokine kinetics for IL-1β, IL-12, TNF, IFN-γ, IL-2, and IL-4, although the levels were lower for CHIKV patients. This study suggests that chemokines may have an important role in the immunopathogenesis of chronic chikungunya-related arthralgia.

Global Geographical and Temporal Patterns of Seasonal Influenza and Associated Climatic Factors

Understanding geographical and temporal patterns of seasonal influenza can help strengthen influenza surveillance to early detect epidemics and inform influenza prevention and control programs. We examined variations in spatiotemporal patterns of seasonal influenza in different global regions and explored climatic factors that influence differences in influenza seasonality, through a systematic review of peer-reviewed publications. The literature search was conducted to identify original studies published between January 2005 and November 2016. Studies were selected using predetermined inclusion and exclusion criteria. The primary outcome was influenza cases; additional outcomes included seasonal or temporal patterns of influenza seasonality, study regions (temperate or tropical), and associated climatic factors. Of the 2,160 records identified in the selection process, 36 eligible studies were included. There were significant differences in influenza seasonality in terms of the time of onset, duration, number of peaks, and amplitude of epidemics between temperate and tropical/subtropical regions. Different viral types, cocirculation of influenza viruses, and climatic factors, especially temperature and absolute humidity, contributed to the variations in spatiotemporal patterns of seasonal influenza. The findings reported in this review could inform global surveillance of seasonal influenza and influenza prevention and control measures such as vaccination recommendations for different regions.

Seasonal patterns of dengue fever in rural Ecuador: 2009-2016

Season is a major determinant of infectious disease rates, including arboviruses spread by mosquitoes, such as dengue, chikungunya, and Zika. Seasonal patterns of disease are driven by a combination of climatic or environmental factors, such as temperature or rainfall, and human behavioral time trends, such as school year schedules, holidays, and weekday-weekend patterns. These factors affect both disease rates and healthcare-seeking behavior. Seasonality of dengue fever has been studied in the context of climatic factors, but short- and long-term time trends are less well-understood. With 2009–2016 medical record data from patients diagnosed with dengue fever at two hospitals in rural Ecuador, we used Poisson generalized linear modeling to determine short- and long-term seasonal patterns of dengue fever, as well as the effect of day of the week and public holidays. In a subset analysis, we determined the impact of school schedules on school-aged children. With a separate model, we examined the effect of climate on diagnosis patterns. In the first model, the most important predictors of dengue fever were annual sinusoidal fluctuations in disease, long-term trends (as represented by a spline for the full study duration), day of the week, and hospital. Seasonal trends showed single peaks in case diagnoses, during mid-March. Compared to the average of all days, cases were more likely to be diagnosed on Tuesdays (risk ratio (RR): 1.26, 95% confidence interval (CI) 1.05–1.51) and Thursdays (RR: 1.25, 95% CI 1.02–1.53), and less likely to be diagnosed on Saturdays (RR: 0.81, 95% CI 0.65–1.01) and Sundays (RR: 0.74, 95% CI 0.58–0.95). Public holidays were not significant predictors of dengue fever diagnoses, except for an increase in diagnoses on the day after Christmas (RR: 2.77, 95% CI 1.46–5.24). School schedules did not impact dengue diagnoses in school-aged children. In the climate model, important climate variables included the monthly total precipitation, an interaction between total precipitation and monthly absolute minimum temperature, an interaction between total precipitation and monthly precipitation days, and a three-way interaction between minimum temperature, total precipitation, and precipitation days. This is the first report of long-term dengue fever seasonality in Ecuador, one of few reports from rural patients, and one of very few studies utilizing daily disease reports. These results can inform local disease prevention efforts, public health planning, as well as global and regional models of dengue fever trends.

Dengue fever exhibits a seasonal pattern in many parts of the world, much of which has been attributed to climate and weather. However, additional factors may contribute to dengue seasonality. With 2009–2016 medical record data from rural Ecuador, we studied the short- and long-term seasonal patterns of dengue fever, as well as the effect of school schedules and public holidays. We also examined the effect of climate on dengue. We found that dengue diagnoses peak once per year in mid-March, but that diagnoses are also affected by day of the week. Dengue was also impacted by regional climate and complex interactions between local weather variables. This is the first report of long-term dengue fever seasonality in Ecuador, one of few reports from rural patients, and one of very few studies utilizing daily disease reports. This is the first report on the impacts of school schedules, holidays, and weekday-weekend patterns on dengue diagnoses. These results suggest a potential impact of human behaviors on dengue exposure risk. More broadly, these results can inform local disease prevention efforts and public health planning, as well as global and regional models of dengue fever trends.

Effect of meteorological factors on influenza-like illness from 2012 to 2015 in Huludao, a northeastern city in China

Background

This study aims to describe the epidemiological patterns of influenza-like illness (ILI) in Huludao, China and seek scientific evidence on the link of ILI activity with weather factors.

Methods

Surveillance data of ILI cases between January 2012 and December 2015 was collected in Huludao Central Hospital, meteorological data was obtained from the China Meteorological Data Service Center. Generalized additive model (GAM) was used to seek the relationship between the number of ILI cases and the meteorological factors. Multiple Smoothing parameter estimation was made on the basis of Poisson distribution, where the number of weekly ILI cases was treated as response, and the smoothness of weather was treated as covariates. Lag time was determined by the smallest Akaike information criterion (AIC). Smoothing coefficients were estimated for the prediction of the number of ILI cases.

Results

A total of 29, 622 ILI cases were observed during the study period, with children ILI cases constituted 86.77%. The association between ILI activity and meteorological factors varied across different lag periods. The lag time for average air temperature, maximum air temperature, minimum air temperature, vapor pressure and relative humidity were 2, 2, 1, 1 and 0 weeks, respectively. Average air temperature, maximum air temperature, minimum air temperature, vapor pressure and relative humidity could explain 16.5%, 9.5%, 18.0%, 15.9% and 7.7% of the deviance, respectively. Among the temperature indexes, the minimum temperature played the most important role. The number of ILI cases peaked when minimum temperature was around -13 °C in winter and 18 °C in summer. The number of cases peaked when the relative humidity was equal to 43% and then began to decrease with the increase of relative humidity. When the humidity exceeded 76%, the number of ILI cases began to rise.

Conclusions

The present study first analyzed the relationship between meteorological factors and ILI cases with special consideration of the length of lag period in Huludao, China. Low air temperature and low relative humidity (cold and dry weather condition) played a considerable role in the epidemic pattern of ILI cases. The trend of ILI activity could be possibly predicted by the variation of meteorological factors.

Estimation of the burden of flu-association influenza-like illness visits on total clinic visits through the sentinel influenza monitoring system in Senegal during the 2013-2015 influenza seasons

Knowing the burden of influenza is helpful for policy decisions. Here we estimated the contribution of influenza-like illness (ILI) visits associated with laboratory-confirmed influenza among all clinic visits in a Senegal sentinel network. ILI data from ten sentinel sites were collected from January 2013 to December 2015. ILI was defined as an axillary measured fever of more than 37.5 °C with a cough or a sore throat. Collected nasopharyngeal swabs were tested for influenza viruses by rRT-PCR. Influenza-associated ILI was defined as ILI with laboratory-confirmed influenza. For the influenza disease burden estimation, we used all-case outpatient visits during the study period who sought care at selected sites. Of 4030 ILI outpatients tested, 1022 were influenza positive. The estimated proportional contribution of influenza-associated ILI was, per 100 outpatients, 1.2 (95% CI 1.1-1.3), 0.32 (95% CI 0.28-0.35), 1.11 (95% CI 1.05-1.16) during 2013, 2014, 2015, respectively. The age-specific outpatient visits proportions of influenza-associated ILI were higher among children under 5 years (0.68%, 95% CI: 0.62-0.70). The predominant virus during years 2013 and 2015 was influenza B while A/H3N2 subtype was predominant during 2014. Influenza viruses cause a substantial burden of outpatient visits particularly among children under 5 of age in Senegal and highlight the need of vaccination in risk groups.

Spatiotemporal diffusion of influenza A (H1N1): Starting point and risk factors

Influenza constitutes a major challenge to world health authorities due to high transmissibility and the capacity to generate large epidemics. This study aimed to characterize the diffusion process of influenza A (H1N1) by identifying the starting point of the epidemic as well as climatic and sociodemographic factors associated with the occurrence and intensity of transmission of the disease. The study was carried out in the Brazilian state of Paraná, where H1N1 caused the largest impact. The units of spatial and temporal analysis were the municipality of residence of the cases and the epidemiological weeks of the year 2009, respectively. Under the Bayesian paradigm, parametric inference was performed through a two-part spatiotemporal model and the integrated nested Laplace approximation (INLA) algorithm. We identified the most likely starting points through the effective distance measure based on mobility networks. The proposed estimation methodology allowed for rapid and efficient implementation of the spatiotemporal model, and provided evidence of different patterns for chance of occurrence and risk of influenza throughout the epidemiological weeks. The results indicate the capital city of Curitiba as the probable starting point, and showed that the interventions that focus on municipalities with greater migration and density of people, especially those with higher Human Development Indexes (HDIs) and the presence of municipal air and road transport, could play an important role in mitigation of effects of future influenza pandemics on public health. These results provide important information on the process of introduction and spread of influenza, and could contribute to the identification of priority areas for surveillance as well as establishment of strategic measures for disease prevention and control. The proposed model also allows identification of epidemiological weeks with high chance of influenza occurrence, which can be used as a reference criterion for creating an immunization campaign schedule.

Estimation of community-level influenza-associated illness in a low resource rural setting in India

Objective

To estimate rates of community-level influenza-like-illness (ILI) and influenza-associated ILI in rural north India.

Methods

During 2011, we conducted household-based healthcare utilization surveys (HUS) for any acute medical illness (AMI) in preceding 14days among residents of 28villages of Ballabgarh, in north India. Concurrently, we conducted clinic-based surveillance (CBS) in the area for AMI episodes with illness onset ≤3days and collected nasal and throat swabs for influenza virus testing using real-time polymerase chain reaction. Retrospectively, we applied ILI case definition (measured/reported fever and cough) to HUS and CBS data. We attributed 14days of risk-time per person surveyed in HUS and estimated community ILI rate by dividing the number of ILI cases in HUS by total risk-time. We used CBS data on influenza positivity and applied it to HUS-based community ILI rates by age, month, and clinic type, to estimate the community influenza-associated ILI rates.

Findings

The HUS of 69,369 residents during the year generated risk-time of 3945 person-years (p-y) and identified 150 (5%, 95%CI: 4–6) ILI episodes (38 ILI episodes/1,000 p-y; 95% CI 32–44). Among 1,372 ILI cases enrolled from clinics, 126 (9%; 95% CI 8–11) had laboratory-confirmed influenza (A (H3N2) = 72; B = 54). After adjusting for age, month, and clinic type, overall influenza-associated ILI rate was 4.8/1,000 p-y; rates were highest among children <5 years (13; 95% CI: 4–29) and persons≥60 years (11; 95%CI: 2–30).

Conclusion

We present a novel way to use HUS and CBS data to generate estimates of community burden of influenza. Although the confidence intervals overlapped considerably, higher point estimates for burden among young children and older adults shows the utility for exploring the value of influenza vaccination among target groups.