Global influenza seasonality: reconciling patterns across temperate and tropical regions

Temperature, Humidity and Latitude Analysis to Predict Potential Spread and Seasonality for COVID-19

BACKGROUND

A significant number of infectious diseases display seasonal patterns in their incidence, including human coronaviruses. Betacoronaviruses such as MERS-CoV and SARS-CoV are not thought to be seasonal.

METHODS

We examined climate data from cities with significant community spread of COVID-19 using ERA-5 reanalysis, and compared to areas that are either not affected, or do not have significant community spread.

FINDINGS

To date, Coronavirus Disease 2019 (COVID-19), caused by SARS-CoV-2, has established significant community spread in cities and regions along a narrow east west distribution roughly along the 30-50o N' corridor at consistently similar weather patterns consisting of average temperatures of 5-11oC, combined with low specific (3-6 g/kg) and absolute humidity (4-7 g/m3). There has been a lack of significant community establishment in expected locations that are based only on population proximity and extensive population interaction through travel.

INTERPRETATION

The distribution of significant community outbreaks along restricted latitude, temperature, and humidity are consistent with the behavior of a seasonal respiratory virus. Additionally, we have proposed a simplified model that shows a zone at increased risk for COVID-19 spread. Using weather modeling, it may be possible to predict the regions most likely to be at higher risk of significant community spread of COVID-19 in the upcoming weeks, allowing for concentration of public health efforts on surveillance and containment.

Seasonal pattern of influenza and the association with meteorological factors based on wavelet analysis in Jinan City, Eastern China, 2013-2016

Background

Influenza is a disease under surveillance worldwide with different seasonal patterns in temperate and tropical regions. Previous studies have conducted modeling of influenza seasonality using climate variables. This study aimed to identify potential meteorological factors that are associated with influenza seasonality in Jinan, China.

Methods

Data from three influenza sentinel hospitals and respective climate factors (average temperature, relatively humidity (RH), absolute humidity (AH), sunshine duration, accumulated rainfall and speed of wind), from 2013 to 2016, were collected. Statistical and wavelet analyses were used to explore the epidemiological characteristics of influenza virus and its potential association with climate factors.

Results

The dynamic of influenza was characterized by annual cycle, with remarkable winter epidemic peaks from December to February. Spearman's correlation and wavelet coherence analysis illuminated that temperature, AH and atmospheric pressure were main influencing factors. Multiple wavelet coherence analysis showed that temperature and atmospheric pressure might be the main influencing factors of influenza virus A(H3N2) and influenza virus B, whereas temperature and AH might best shape the seasonality of influenza virus A(H1N1)pdm09. During the epidemic season, the prevalence of influenza virus lagged behind the change of temperature by 1-8 weeks and atmospheric pressure by 0.5-3 weeks for different influenza viruses.

Conclusion

Climate factors were significantly associated with influenza seasonality in Jinan during the influenza epidemic season and the optional time for influenza vaccination is before November. These finding should be considered in influenza planning of control and prevention.

Characteristics of Seasonal Influenza Virus Activity in a Subtropical City in China, 2013-2019

BACKGROUND

To optimize seasonal influenza vaccination programs in regions with potentially complicated seasonal patterns, the epidemiological characteristics of seasonal influenza activity in a subtropical city of China were explored.

MATERIALS AND METHODS

Influenza virus data of patients with influenza-like illness (ILI) during 2013-2019 were collected from two sentinel hospitals in a subtropical region of China, Yichang city. The influenza virus positive rate among sampled ILI cases served as a proxy to estimate influenza seasonal characteristics, including periodicity, duration, peaks, and predominant subtypes/lineages. Epidemiological features of different years, seasons and age groups were analyzed, and vaccine mismatches were identified.

RESULTS

In total, 8693 ILI cases were included; 1439 (16.6%) were laboratory-confirmed influenza cases. The influenza A positive rate (10.6%) was higher than the influenza B positive rate (5.9%). There were three influenza circulation patterns in Yichang: (1) annual periodicity (in 2013-2014, 2015-2016 and 2018-2019), (2) semiannual periodicity (in 2014-2015), and (3) year-round periodicity (in 2016-2017 and 2017-2018). Summer epidemics existed in two of the six years and were dominated by influenza A/H3N2. Winter and spring epidemics occurred in five of the six years, and A/H1N1, A/H3N2, B/Victoria, and B/Yamagata were codominant. During the study period, the predominant lineages, B/Victoria in 2015-16 and B/Yamagata in 2017-2018, were both mismatched with the influenza B component of the trivalent vaccine. Children 5-14 years old (26.4%) and individuals over 60 years old (16.9%) had the highest influenza positive rates.

CONCLUSIONS

The seasonal epidemic period and the predominant subtype/lineage of influenza viruses in Yichang city are complex. Influenza vaccination timing and strategies need to be optimized according to the local features of influenza virus activity.

Latitudes mediate the association between influenza activity and meteorological factors: A nationwide modelling analysis in 45 Japanese prefectures from 2000 to 2018

BACKGROUND

Cold and dry conditions were well-documented as a major determinant of influenza seasonality in temperate countries but the association may not be consistent when the climate in temperate areas is closer to that in sub-tropical areas. We hypothesized latitudes may mediate the association between influenza activity and meteorological factors in 45 Japanese prefectures.

METHODS

We used the weekly incidence of influenza-like illness of 45 prefectures from 2000 to 2018 as a proxy for influenza activity in Japan, a temperate country lying off the east coast of Asia. A combination of generalized additive model and distributed lag nonlinear model was adopted to investigate the associations between meteorological factors (average temperature, relative humidity, total rainfall, and actual vapour pressure, a proxy for absolute humidity) and the influenza incidence. Kendall's tau b (τ) and Spearman correlation coefficient (r_s) between latitude and the adjusted relative risk (ARR) of each meteorological factor were also assessed.

RESULTS

A higher vapour pressure was significantly associated with a lower influenza risk but the ARR strongly weakened along with a lower latitude (τ = -0.23, p-value = 0.02; r_s = -0.33, p-value = 0.03). Lower temperature and lower relatively humidity were significantly associated with higher influenza risks in over 65% and around 40% of the prefectures respectively but the strength and significance of the correlations between their ARRs and latitude were weaker than that from vapour pressure.

CONCLUSION

Even though the range of latitudes in Japan is small (26°N-43°N), the relationships between meteorological factors and influenza activity were mediated by the latitude. Our study echoed absolute humidity played a more important role in relating influenza risk, but we on the other hand showed its effect on influenza activity could be hampered in a low-latitude temperate region, which have a warmer climate. These findings thus offer a high-resolution characterization of the role of meteorological factors on influenza seasonality.

Humidity-Dependent Decay of Viruses, but Not Bacteria, in Aerosols and Droplets Follows Disinfection Kinetics

The transmission of some infectious diseases requires that pathogens can survive (i.e., remain infectious) in the environment, outside the host. Relative humidity (RH) is known to affect the survival of some microorganisms in the environment; however, the mechanism underlying the relationship has not been explained, particularly for viruses. We investigated the effects of RH on the viability of bacteria and viruses in both suspended aerosols and stationary droplets using traditional culture-based approaches. Results showed that viability of bacteria generally decreased with decreasing RH. Viruses survived well at RHs lower than 33% and at 100%, whereas their viability was reduced at intermediate RHs. We then explored the evaporation rate of droplets consisting of culture media and the resulting changes in solute concentrations over time; as water evaporates from the droplets, solutes such as sodium chloride in the media become more concentrated. Based on the results, we suggest that inactivation of bacteria is influenced by osmotic pressure resulting from elevated concentrations of salts as droplets evaporate. We propose that the inactivation of viruses is governed by the cumulative dose of solutes or the product of concentration and time, as in disinfection kinetics. These findings emphasize that evaporation kinetics play a role in modulating the survival of microorganisms in droplets.

Global dynamic spatiotemporal pattern of seasonal influenza since 2009 influenza pandemic

Background

Understanding the global spatiotemporal pattern of seasonal influenza is essential for influenza control and prevention. Available data on the updated global spatiotemporal pattern of seasonal influenza are scarce. This study aimed to assess the spatiotemporal pattern of seasonal influenza after the 2009 influenza pandemic.

Methods

Weekly influenza surveillance data in 86 countries from 2010 to 2017 were obtained from FluNet. First, the proportion of influenza A in total influenza viruses (P_A) was calculated. Second, weekly numbers of influenza positive virus (A and B) were divided by the total number of samples processed to get weekly positive rates of influenza A (RW_A) and influenza B (RW_B). Third, the average positive rates of influenza A (R_A) and influenza B (R_B) for each country were calculated by averaging RW_A, and RW_B of 52 weeks. A Kruskal-Wallis test was conducted to examine if the year-to-year change in P_A in all countries were significant, and a universal kriging method with linear semivariogram model was used to extrapolate R_A and R_B in all countries.

Results

P_A ranged from 0.43 in Zambia to 0.98 in Belarus, and P_A in countries with higher income was greater than those countries with lower income. The spatial patterns of high R_B were the highest in sub-Saharan Africa, Asia-Pacific region and South America. RW_A peaked in early weeks in temperate countries, and the peak of RW_B occurred a bit later. There were some temperate countries with non-distinct influenza seasonality (e.g., Mauritius and Maldives) and some tropical/subtropical countries with distinct influenza seasonality (e.g., Chile and South Africa).

Conclusions

Influenza seasonality is not predictable in some temperate countries, and it is distinct in Chile, Argentina and South Africa, implying that the optimal timing for influenza vaccination needs to be chosen with caution in these unpredictable countries.

Influenza activity prediction using meteorological factors in a warm temperate to subtropical transitional zone, Eastern China

Influenza activity is subject to environmental factors. Accurate forecasting of influenza epidemics would permit timely and effective implementation of public health interventions, but it remains challenging. In this study, we aimed to develop random forest (RF) regression models including meterological factors to predict seasonal influenza activity in Jiangsu provine, China. Coefficient of determination (R2) and mean absolute percentage error (MAPE) were employed to evaluate the models' performance. Three RF models with optimum parameters were constructed to predict influenza like illness (ILI) activity, influenza A and B (Flu-A and Flu-B) positive rates in Jiangsu. The models for Flu-B and ILI presented excellent performance with MAPEs <10%. The predicted values of the Flu-A model also matched the real trend very well, although its MAPE reached to 19.49% in the test set. The lagged dependent variables were vital predictors in each model. Seasonality was more pronounced in the models for ILI and Flu-A. The modification effects of the meteorological factors and their lagged terms on the prediction accuracy differed across the three models, while temperature always played an important role. Notably, atmospheric pressure made a major contribution to ILI and Flu-B forecasting. In brief, RF models performed well in influenza activity prediction. Impacts of meteorological factors on the predictive models for influenza activity are type-specific.

Epidemic dynamics of respiratory syncytial virus in current and future climates

A key question for infectious disease dynamics is the impact of the climate on future burden. Here, we evaluate the climate drivers of respiratory syncytial virus (RSV), an important determinant of disease in young children. We combine a dataset of county-level observations from the US with state-level observations from Mexico, spanning much of the global range of climatological conditions. Using a combination of nonlinear epidemic models with statistical techniques, we find consistent patterns of climate drivers at a continental scale explaining latitudinal differences in the dynamics and timing of local epidemics. Strikingly, estimated effects of precipitation and humidity on transmission mirror prior results for influenza. We couple our model with projections for future climate, to show that temperature-driven increases to humidity may lead to a northward shift in the dynamic patterns observed and that the likelihood of severe outbreaks of RSV hinges on projections for extreme rainfall.

Influenza Virus Infectivity Is Retained in Aerosols and Droplets Independent of Relative Humidity

Pandemic and seasonal influenza viruses can be transmitted through aerosols and droplets, in which viruses must remain stable and infectious across a wide range of environmental conditions. Using humidity-controlled chambers, we studied the impact of relative humidity on the stability of 2009 pandemic influenza A(H1N1) virus in suspended aerosols and stationary droplets. Contrary to the prevailing paradigm that humidity modulates the stability of respiratory viruses in aerosols, we found that viruses supplemented with material from the apical surface of differentiated primary human airway epithelial cells remained equally infectious for 1 hour at all relative humidities tested. This sustained infectivity was observed in both fine aerosols and stationary droplets. Our data suggest, for the first time, that influenza viruses remain highly stable and infectious in aerosols across a wide range of relative humidities. These results have significant implications for understanding the mechanisms of transmission of influenza and its seasonality.

Environmental Persistence of Influenza Viruses Is Dependent upon Virus Type and Host Origin

Highly transmissible influenza viruses (IV) must remain stable and infectious under a wide range of environmental conditions following release from the respiratory tract into the air. Understanding how expelled IV persist in the environment is critical to limiting the spread of these viruses. Little is known about how the stability of different IV in expelled aerosols is impacted by exposure to environmental stressors, such as relative humidity (RH). Given that not all IV are equally capable of efficient airborne transmission in people, we anticipated that not all IV would respond uniformly to ambient RH. Therefore, we have examined the stability of human-pathogenic seasonal and avian IV in suspended aerosols and stationary droplets under a range of RH conditions. H3N2 and influenza B virus (IBV) isolates are resistant to RH-dependent decay in aerosols in the presence of human airway surface liquid, but we observed strain-dependent variations in the longevities of H1N1, H3N2, and IBV in droplets. Surprisingly, low-pathogenicity avian influenza H6N1 and H9N2 viruses, which cause sporadic infections in humans but are unable to transmit person to person, demonstrated a trend toward increased sensitivity at midrange to high-range RH. Taken together, our observations suggest that the levels of vulnerability to decay at midrange RH differ with virus type and host origin.IMPORTANCE The rapid spread of influenza viruses (IV) from person to person during seasonal epidemics causes acute respiratory infections that can lead to hospitalizations and life-threatening illness. Atmospheric conditions such as relative humidity (RH) can impact the viability of IV released into the air. To understand how different IV are affected by their environment, we compared the levels of stability of human-pathogenic seasonal and avian IV under a range of RH conditions and found that highly transmissible seasonal IV were less sensitive to decay under midrange RH conditions in droplets. We observed that certain RH conditions can support the persistence of infectious viruses on surfaces and in the air for extended periods of time. Together, our findings will facilitate understanding of factors affecting the persistence and spread of IV in our environment.

Seasonality of influenza and its association with meteorological parameters in two cities of Pakistan: A time series analysis

Background

Influenza is known to have a specific pattern of seasonality the reasons for which are yet to be fully ascertained. Temperate zones show influenza epidemic during the winter months. The tropical and subtropical regions show more diverse influenza outbreak patterns. This study explores the seasonality of influenza activity and predicts influenza peak based on historical surveillance time series data in Islamabad and Multan, Pakistan.

Methods

This is a descriptive study of routinely collected monthly influenza sentinel surveillance data and meteorological data from 2012–16 in two sentinel sites of Pakistan: Islamabad (North) and Multan (Central).

Results

Mean number of cases of influenza and levels of precipitation were higher in Islamabad compared to Multan. Mean temperature and humidity levels were similar in both the cities. The number of influenza cases rose with decrease in precipitation and temperature in Islamabad during 2012–16, although the same cannot be said about humidity. The relationship between meteorological parameters and influenza incidence was not pronounced in case of Multan. The forecasted values in both the cities showed a significant peak during the month of January.

Conclusion

The influenza surveillance system gave a better understanding of the disease trend and could accurately forecast influenza activity in Pakistan.

National retrospective cohort study to identify age-specific fatality risks of comorbidities among hospitalised patients with influenza-like illness in Taiwan

OBJECTIVES

This study aimed to examine comprehensively the prognostic impact of underlying comorbidities among hospitalised patients with influenza-like illness (ILI) in different age groups and provide recommendations targeting the vulnerable patients.

SETTING AND PARTICIPANTS

A retrospective cohort of 83 227 hospitalised cases with ILI were identified from Taiwan's National Health Insurance Research Database from January 2005 to December 2010. Cases were stratified into three different age groups: paediatric (0-17 years), adult (18-64 years) and elderly (≧65 years), and their age, sex, comorbidity and past healthcare utilisation were analysed for ILI-associated fatality.

MAIN OUTCOME MEASURES

ORs for ILI-related fatality in different age groups were performed using multivariable analyses with generalised estimating equation models and adjusted by age, sex and underlying comorbidities.

RESULTS

Hospitalised ILI-related fatality significantly increased with comorbidities of cancer with metastasis (adjusted OR (aOR)=3.49, 95% CI: 3.16 to 3.86), haematological malignancy (aOR=3.02, 95% CI: 2.43 to 3.74), cancer without metastasis (aOR=1.72, 95% CI: 1.54 to 1.91), cerebrovascular (aOR=1.24, 95% CI: 1.15 to 1.33) and heart diseases (aOR=1.19, 95% CI: 1.11 to 1.27) for all age groups. Adult patients with AIDS; adult and elderly patients with chronic kidney disease, tuberculosis and diabetes were significantly associated with elevated risk of death. Severe liver diseases and hypothyroidism among elderly, and dementia/epilepsy among elderly and paediatrics were distinctively associated with likelihood of ILI-related fatality.

CONCLUSIONS

Different age-specific comorbidities were associated with increasing risk of death among hospitalised ILI patients. These findings may help update guidelines for influenza vaccination and other prevention strategies in high-risk groups for minimising worldwide ILI-related deaths.

Low ambient humidity impairs barrier function and innate resistance against influenza infection

Influenza virus causes seasonal outbreaks in temperate regions, with an increase in disease and mortality in the winter months. Dry air combined with cold temperature is known to enable viral transmission. In this study, we asked whether humidity impacts the host response to influenza virus infections. Exposure of mice to low humidity conditions rendered them more susceptible to influenza disease. Mice housed in dry air had impaired mucociliary clearance, innate antiviral defense, and tissue repair function. Moreover, mice exposed to dry air were more susceptible to disease mediated by inflammasome caspases. Our study provides mechanistic insights for the seasonality of the influenza virus epidemics, whereby inhalation of dry air compromises the host’s ability to restrict influenza virus infection.

In the temperate regions, seasonal influenza virus outbreaks correlate closely with decreases in humidity. While low ambient humidity is known to enhance viral transmission, its impact on host response to influenza virus infection and disease outcome remains unclear. Here, we showed that housing Mx1 congenic mice in low relative humidity makes mice more susceptible to severe disease following respiratory challenge with influenza A virus. We find that inhalation of dry air impairs mucociliary clearance, innate antiviral defense, and tissue repair. Moreover, disease exacerbated by low relative humidity was ameliorated in caspase-1/11–deficient Mx1 mice, independent of viral burden. Single-cell RNA sequencing revealed that induction of IFN-stimulated genes in response to viral infection was diminished in multiple cell types in the lung of mice housed in low humidity condition. These results indicate that exposure to dry air impairs host defense against influenza infection, reduces tissue repair, and inflicts caspase-dependent disease pathology.

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.

Regional patterns of genetic diversity in swine influenza A viruses in the United States from 2010 to 2016

BACKGROUND

Regular spatial and temporal analyses of the genetic diversity and evolutionary patterns of influenza A virus (IAV) in swine inform control efforts and improve animal health. Initiated in 2009, the USDA passively surveils IAV in U.S. swine, with a focus on subtyping clinical respiratory submissions, sequencing the hemagglutinin (HA) and neuraminidase (NA) genes at a minimum, and sharing these data publicly.

OBJECTIVES

In this study, our goal was to quantify and describe regional and national patterns in the genetic diversity and evolution of IAV in U.S. swine from 2010 to 2016.

METHODS

A comprehensive phylogenetic and epidemiological analysis of publicly available HA and NA genes generated by the USDA surveillance system collected from January 2010 to December 2016 was conducted.

RESULTS

The dominant subtypes and genetic clades detected during the study period were H1N1 (H1-γ/1A.3.3.3, N1-classical, 29%), H1N2 (H1-δ1/1B.2.2, N2-2002, 27%), and H3N2 (H3-IV-A, N2-2002, 15%), but many other minor clades were also maintained. Year-round circulation was observed, with a primary epidemic peak in October-November and a secondary epidemic peak in March-April. Partitioning these data into 5 spatial zones revealed that genetic diversity varied regionally and was not correlated with aggregated national patterns of HA/NA diversity.

CONCLUSIONS

These data suggest that vaccine composition and control efforts should consider IAV diversity within swine production regions in addition to aggregated national patterns.

Effects and interaction of meteorological factors on influenza: Based on the surveillance data in Shaoyang, China

BACKGROUND

Previous studies have demonstrated that meteorological factors influence the incidence of influenza. However, little is known regarding the interactions of meteorological factors on the risk of influenza in China.

OBJECTIVE

The study aimed to evaluate the associations between meteorological factors and influenza in Shaoyang of southern China, and explore the interaction of temperature with humidity and rainfall.

METHODS

Weekly meteorological data and disease surveillance data of influenza in Shaoyang were collected from 2009 to 2012. According to the incubation period and infectious period of influenza virus, the maximum lag period was set as 3 weeks. A generalized additive model was conducted to evaluate the effect of meteorological factors on the weekly number of influenza cases and a stratification model was applied to investigate the interaction.

RESULTS

During the study period, the total number of influenza cases that were notified in the study area was 2506, with peak times occurring from December to March. After controlling for the confounders, each 5 °C decrease in minimum temperature was related to 8% (95%CI: 1-15%) increase in the number of influenza cases at a 1-week lag. There was an interaction between minimum temperature and relative humidity and the risk of influenza was higher in cold and less humid conditions than other conditions. The interaction between minimum temperature and rainfall was not statistically significant in our study.

CONCLUSIONS

The study suggests that minimum temperature is inversely associated with influenza in the study area of China, and the effect can be modified by relative humidity. Meteorological variables could be integrated in current public health surveillance system to better prepare for the risks of influenza.

Early-life exposure to weather shocks and child height: Evidence from industrializing Japan

In this study, we estimate the long-run effects of early-life exposure to weather shocks on the height of primary school children. To estimate the global impacts on almost the entire child population in an industrializing country, we utilize both a unique nationwide multi-dimensional longitudinal dataset of Japanese children aged 6-11 and official monthly statistics on meteorological conditions in the 1920s. We observe that the exposure to cold waves in early-life exerted stunting effects on both the boys and girls. In the coldest regions in the northeastern area of Japan, the stunting effects of cold weather shocks on the boys and girls are estimated to be approximately 0.8 and 0.6 cm, respectively. Our observation indicates that prenatal (postnatal) exposure is important for the boys (girls). Our results suggest that the marginal effects of cold waves are stronger in the warmer regions than in the colder regions.

Effects of Absolute Humidity, Relative Humidity, Temperature, and Wind Speed on Influenza Activity in Toronto, Ontario, Canada

The occurrence of influenza in different climates has been shown to be associated with multiple meteorological factors. The incidence of influenza has been reported to increase during rainy seasons in tropical climates and during the dry, cold months of winter in temperate climates. This study was designed to explore the role of absolute humidity (AH), relative humidity (RH), temperature, and wind speed (WS) on influenza activity in the Toronto, ON, Canada, area. Environmental data obtained from four meteorological stations in the Toronto area over the period from 1 January 2010 to 31 December 2015 were linked to patient influenza data obtained for the same locality and period. Data were analyzed using correlation, negative binomial regressions with linear predictors, and splines to capture the nonlinear relationship between exposure and outcomes. Our study found a negative association of both AH and temperature with influenza A and B virus infections. The effect of RH on influenza A and B viruses was controversial. Temperature fluctuation was associated with increased numbers of influenza B virus infections. Influenza virus was less likely to be detected from community patients than from patients tested as part of an institutional outbreak investigation. This could be more indicative of nosocomial transmission rather than climactic factors. The nonlinear nature of the relationship of influenza A virus with temperature and of influenza B virus with AH, RH, and temperature could explain the complexity and variation between influenza A and B virus infections. Predicting influenza activity is important for the timing of implementation of disease prevention and control measures as well as for resource allocation.IMPORTANCE This study examined the relationship between environmental factors and the occurrence of influenza in general. Since the seasonality of influenza A and B viruses is different in most temperate climates, we also examined each influenza virus separately. This study reports a negative association of both absolute humidity and temperature with influenza A and B viruses and tries to understand the controversial effect of RH on influenza A and B viruses. This study reports a nonlinear relation between influenza A and B viruses with temperature and influenza B virus with absolute and relative humidity. The nonlinear nature of these relations could explain the complexity and difference in seasonality between influenza A and B viruses, with the latter predominating later in the season. Separating community-based specimens from those obtained during outbreaks was also a novel approach in this research. These findings provide a further understanding of influenza virus transmission in temperate climates.

Association of meteorological factors with seasonal activity of influenza A subtypes and B lineages in subtropical western China

The seasonality of individual influenza subtypes/lineages and the association of influenza epidemics with meteorological factors in the tropics/subtropics have not been well understood. The impact of the 2009 H1N1 pandemic on the prevalence of seasonal influenza virus remains to be explored. Using wavelet analysis, the periodicities of A/H3N2, seasonal A/H1N1, A/H1N1pdm09, Victoria and Yamagata were identified, respectively, in Panzhihua during 2006–2015. As a subtropical city in southwestern China, Panzhihua is the first industrial city in the upper reaches of the Yangtze River. The relationship between influenza epidemics and local climatic variables was examined based on regression models. The temporal distribution of influenza subtypes/lineages during the pre-pandemic (2006–2009), pandemic (2009) and post-pandemic (2010–2015) years was described and compared. A total of 6892 respiratory specimens were collected and 737 influenza viruses were isolated. A/H3N2 showed an annual cycle with a peak in summer–autumn, while A/H1N1pdm09, Victoria and Yamagata exhibited an annual cycle with a peak in winter–spring. Regression analyses demonstrated that relative humidity was positively associated with A/H3N2 activity while negatively associated with Victoria activity. Higher prevalence of A/H1N1pdm09 and Yamagata was driven by lower absolute humidity. The role of weather conditions in regulating influenza epidemics could be complicated since the diverse viral transmission modes and mechanism. Differences in seasonality and different associations with meteorological factors by influenza subtypes/lineages should be considered in epidemiological studies in the tropics/subtropics. The development of subtype- and lineage-specific prevention and control measures is of significant importance.

Seasonality and community interventions in a mathematical model of Clostridium difficile transmission

BACKGROUND

Clostridium difficile infection (CDI) is the leading cause of antibiotic-associated diarrhoea with peak incidence in late winter or early autumn. Although CDI is commonly associated with hospitals, community transmission is important.

AIM

To explore potential drivers of CDI seasonality and the effect of community-based interventions to reduce transmission.

METHODS

A mechanistic compartmental model of C. difficile transmission in a hospital and surrounding community was used to determine the effect of reducing transmission or antibiotic prescriptions in these settings. The model was extended to allow for seasonal antibiotic prescriptions and seasonal transmission.

FINDINGS

Modelling antibiotic seasonality reproduced the seasonality of CDI, including approximate magnitude (13.9-15.1% above annual mean) and timing of peaks (0.7-1.0 months after peak antibiotics). Halving seasonal excess prescriptions reduced the incidence of CDI by 6-18%. Seasonal transmission produced larger seasonal peaks in the prevalence of community colonization (14.8-22.1% above mean) than seasonal antibiotic prescriptions (0.2-1.7% above mean). Reducing transmission from symptomatic or hospitalized patients had little effect on community-acquired CDI, but reducing transmission in the community by ≥7% or transmission from infants by ≥30% eliminated the pathogen. Reducing antibiotic prescription rates led to approximately proportional reductions in infections, but limited reductions in the prevalence of colonization.

CONCLUSION

Seasonal variation in antibiotic prescription rates can account for the observed magnitude and timing of C. difficile seasonality. Even complete prevention of transmission from hospitalized patients or symptomatic patients cannot eliminate the pathogen, but interventions to reduce transmission from community residents or infants could have a large impact on both hospital- and community-acquired infections.

Seasonal characteristics of influenza vary regionally across US

Given substantial regional differences in absolute humidity across the US and our understanding of the relationship between absolute humidity and influenza, we may expect important differences in regional seasonal influenza activity. Here, we assessed cross-seasonal influenza activity by comparing counts of positive influenza A and B rapid test results during the influenza season versus summer baseline periods for the 2016/2017 and 2017/2018 influenza years. Our analysis indicates significant regional patterns in cross-seasonal influenza activity, with relatively fewer influenza cases during the influenza season compared to summertime baseline periods in humid areas of the US, particularly in Florida and Hawaii. The cross-seasonal ratios vary from year-to-year and influenza type, but the geographic patterning of the ratios is relatively consistent. Mixed-effects regression models indicated absolute humidity during the influenza season was the strongest predictor of cross-seasonal influenza activity, suggesting a relationship between absolute humidity and cross-seasonal influenza activity. There was also evidence that absolute humidity during the summer plays a role, as well. This analysis suggests that spatial variation in seasonal absolute humidity levels may generate important regional differences in seasonal influenza activity and dynamics in the US.

Fogarty International Center collaborative networks in infectious disease modeling: Lessons learnt in research and capacity building

Due to a combination of ecological, political, and demographic factors, the emergence of novel pathogens has been increasingly observed in animals and humans in recent decades. Enhancing global capacity to study and interpret infectious disease surveillance data, and to develop data-driven computational models to guide policy, represents one of the most cost-effective, and yet overlooked, ways to prepare for the next pandemic. Epidemiological and behavioral data from recent pandemics and historic scourges have provided rich opportunities for validation of computational models, while new sequencing technologies and the 'big data' revolution present new tools for studying the epidemiology of outbreaks in real time. For the past two decades, the Division of International Epidemiology and Population Studies (DIEPS) of the NIH Fogarty International Center has spearheaded two synergistic programs to better understand and devise control strategies for global infectious disease threats. The Multinational Influenza Seasonal Mortality Study (MISMS) has strengthened global capacity to study the epidemiology and evolutionary dynamics of influenza viruses in 80 countries by organizing international research activities and training workshops. The Research and Policy in Infectious Disease Dynamics (RAPIDD) program and its precursor activities has established a network of global experts in infectious disease modeling operating at the research-policy interface, with collaborators in 78 countries. These activities have provided evidence-based recommendations for disease control, including during large-scale outbreaks of pandemic influenza, Ebola and Zika virus. Together, these programs have coordinated international collaborative networks to advance the study of emerging disease threats and the field of computational epidemic modeling. A global community of researchers and policy-makers have used the tools and trainings developed by these programs to interpret infectious disease patterns in their countries, understand modeling concepts, and inform control policies. Here we reflect on the scientific achievements and lessons learnt from these programs (h-index = 106 for RAPIDD and 79 for MISMS), including the identification of outstanding researchers and fellows; funding flexibility for timely research workshops and working groups (particularly relative to more traditional investigator-based grant programs); emphasis on group activities such as large-scale modeling reviews, model comparisons, forecasting challenges and special journal issues; strong quality control with a light touch on outputs; and prominence of training, data-sharing, and joint publications.

A maximum curvature method for estimating epidemic onset of seasonal influenza in Japan

BACKGROUND

Detecting the onset of influenza epidemic is important for epidemiological surveillance and for investigating the factors driving spatiotemporal transmission patterns. Most approaches define the epidemic onset based on thresholds, which use subjective criteria and are specific to individual surveillance systems.

METHODS

We applied the empirical threshold method (ETM), together with two non-thresholding methods, including the maximum curvature method (MCM) that we proposed and the segmented regression method (SRM), to determine onsets of influenza epidemics in each prefecture of Japan, using sentinel surveillance data of influenza-like illness (ILI) from 2012/2013 through 2017/2018. Performance of the MCM and SRM was evaluated, in terms of epidemic onset, end, and duration, with those derived from the ETM using the nationwide epidemic onset indicator of 1.0 ILI case per sentinel per week.

RESULTS

The MCM and SRM yielded complete estimates for each of Japan's 47 prefectures. In contrast, ETM estimates for Kagoshima during 2012/2013 and for Okinawa during all six influenza seasons, except 2013/2014, were invalid. The MCM showed better agreement in all estimates with the ETM than the SRM (R2 = 0.82, p < 0.001 vs. R2 = 0.34, p < 0.001 for epidemic onset; R2 = 0.18, p < 0.001 vs. R2 = 0.05, p < 0.001 for epidemic end; R2 = 0.28, p < 0.001 vs. R2 < 0.01, p = 0.35 for epidemic duration). Prefecture-specific thresholds for epidemic onset and end were established using the MCM.

CONCLUSIONS

The Japanese national epidemic onset threshold is not applicable to all prefectures, particularly Okinawa. The MCM could be used to establish prefecture-specific epidemic thresholds that faithfully characterize influenza activity, serving as useful complements to the influenza surveillance system in Japan.

Temporal patterns of influenza A subtypes and B lineages across age in a subtropical city, during pre-pandemic, pandemic, and post-pandemic seasons

Background

Seasonal patterns of influenza A subtypes and B lineages in tropical/subtropical regions across age have remained to be explored. The impact of the 2009 H1N1 pandemic on seasonal influenza activity have not been well understood.

Methods

Based on a national sentinel hospital-based influenza surveillance system, the epidemiology of influenza virus during 2006/07–2015/16 was characterized in the subtropical city, Chengdu. Chengdu is one of the most populous cities in southwestern China, where the first reported case of A/H1N1pdm09 in mainland China was identified. Wavelet analysis was applied to identify the periodicities of A/H3N2, seasonal A/H1N1, A/H1N1pdm09, Victoria, and Yamagata across age, respectively. The persistence and age distribution patterns were described during the pre-pandemic (2006/07–2008/09), pandemic (2009/10), and post-pandemic (2010/11–2015/16) seasons.

Results

A total of 10,981 respiratory specimens were collected, of which 2516 influenza cases were identified. Periodicity transition from semi-annual cycles to an annual cycle was observed for composite influenza virus as well as A/H3N2 along in Chengdu since the 2009 H1N1 pandemic. Semi-annual cycles of composite influenza virus and A/H3N2 along were observed again during 2014/15–2015/16, coinciding with the emergence and predominance of A/H3N2 significant antigenic drift groups. However, A/H1N1pdm09, Victoria, and Yamagata generally demonstrated an annual winter-spring peak in non-pandemic seasons. Along with periodicity transitions, age groups with higher positive rates shifted from school-aged children and adults to adults and the elderly for A/H1N1pdm09 during 2009/10–2010/11 and for A/H3N2 during 2014/15–2015/16.

Conclusions

Differences in periodicity and age distribution by subtype/lineage and by season highlight the importance of increasing year-round influenza surveillance and developing subtype/lineage- and age-specific prevention and control measures. Changes of periodicity and age shifts should be considered in public health response to influenza pandemics and epidemics. In addition, it is suggested to use quadrivalent influenza vaccines to provide protection against both influenza B lineages.

Electronic supplementary material

The online version of this article (10.1186/s12879-019-3689-9) contains supplementary material, which is available to authorized users.

Effects of air pollutants on occurrences of influenza-like illness and laboratory-confirmed influenza in Hefei, China

Accumulating evidence suggests that air pollution is a risk factor for adverse respiratory and cardiovascular health outcomes. However, the different impacts of exposure to air pollutants on influenza virus activity and influenza-like illness (ILI) have not been well documented in epidemiological studies. We examined the association between air pollutants of particular matters < 2.5 μm (PM_2.5), particular matters < 10 μm (PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and influenza occurrences in Hefei, China, from December 2013 to December 2015 by generalized Poisson additive regression models. The result suggested that PM_2.5 and PM₁₀ had similar effects on clinical ILI and influenza incidence. PM₁₀ was negatively associated with clinical ILI (relative risk (RR) 0.980, 95% confidence interval (CI) 0.974-0.987), while PM_2.5 were positively associated with clinical ILI (RR 1.040; 95% CI 1.032-1.049). RRs for the laboratory-confirmed cases of influenza were 0.813 (95% CI, 0.755-0.875) for PM₁₀ and 1.216 (95% CI, 1.134-1.304) for PM_2.5. Nevertheless, the impacts of SO₂ and NO₂ on ILI and influenza were distinct. SO₂ had significant influence on laboratory-confirmed influenza and had no significant linear relationship with ILI. NO₂ was negatively correlated with influenza but had no obvious effect on clinical ILI cases. The present study contributes novel evidence on understanding of the effects of various air pollutants on influenza activities, and these findings can be useful and important for the development of influenza surveillance and early warning systems.

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.

The effect of ambient temperature on the activity of influenza and influenza like illness in Jiangsu Province, China

OBJECTIVE

We aimed to evaluate and quantify the association between ambient temperature and activity of influenza like illness (ILI) and influenza in Jiangsu Province, China.

METHOD

Daily data of meteorology, influenza-like illness and detected influenza virus from 1 April 2013 to 27 March 2016 were collected. Distributed lag non-linear model (DLNM) was used to quantify the exposure-lag-response of ILI and influenza activity to daily average temperature.

RESULT

Influenza A virus (Flu-A) circulated throughout the year with two peaks at -4 °C and 28 °C respectively, while influenza B (Flu-B) viruses were usually tested positive in winter or early spring and peaked at 5 °C. The lag-response curves revealed that the RR of ILI increased with time and peaked 1 day later at low temperature (3 °C), however, the maximum RR of ILI caused by high temperature (26 °C) appeared immediately on day 0, the similar phenomena of immediate effect to ILI at high temperature were also observed in the lag-response curve for Flu-A or Flu-B.

CONCLUSION

ILI and Flu-A experienced two peaks of circulates at both low and high temperature in Jiangsu. The influenza viruses activity did drive up the rising of ILI%, particularly the activity of Flu-A which circulated throughout the year played a crucial role. Regional homogeneity was the relatively mainstream in aspects of cumulative association between influenza activity and temperature in Jiangsu Province.

Spatiotemporal Patterns and Diffusion of the 1918 Influenza Pandemic in British India

The factors that drive spatial heterogeneity and diffusion of pandemic influenza remain debated. We characterized the spatiotemporal mortality patterns of the 1918 influenza pandemic in British India and studied the role of demographic factors, environmental variables, and mobility processes on the observed patterns of spread. Fever-related and all-cause excess mortality data across 206 districts in India from January 1916 to December 1920 were analyzed while controlling for variation in seasonality particular to India. Aspects of the 1918 autumn wave in India matched signature features of influenza pandemics, with high disease burden among young adults, (moderate) spatial heterogeneity in burden, and highly synchronized outbreaks across the country deviating from annual seasonality. Importantly, we found population density and rainfall explained the spatial variation in excess mortality, and long-distance travel via railroad was predictive of the observed spatial diffusion of disease. A spatiotemporal analysis of mortality patterns during the 1918 influenza pandemic in India was integrated in this study with data on underlying factors and processes to reveal transmission mechanisms in a large, intensely connected setting with significant climatic variability. The characterization of such heterogeneity during historical pandemics is crucial to prepare for future pandemics.

Timing of respiratory syncytial virus and influenza epidemic activity in five regions of Argentina, 2007-2016

Background

Within‐country differences in the timing of RSV and influenza epidemics have not been assessed in Argentina, the eighth largest country in the world by area.

Objective

We aimed to compare seasonality for RSV and influenza both nationally and in each of the five regions to inform Argentina’s prevention and treatment guidelines.

Method

The Argentine National Laboratories and Health Institutes Administration collected respiratory specimens from clinical practices, outbreak investigations, and respiratory virus surveillance in 2007‐2016; these were tested using immunofluorescence or RT‐PCR techniques. We calculated weekly percent positive (PP) and defined season onset as >2 consecutive weeks when PP exceeded the annual mean for the respective year and region. Median season measures (onset, offset and peak) and the established mean method were calculated for each virus.

Results

An annual median 59 396 specimens were tested for RSV and 60 931 for influenza; 21–29% tested positive for RSV and 2–7% for influenza. National RSV activity began in April; region‐specific start weeks varied by 7 weeks. Duration of RSV activity did not vary widely by region (16–18 weeks in duration). National influenza activity started in June; region‐specific start weeks varied by 3 weeks. Duration of influenza epidemic activity varied more by region than that of RSV (7–13 weeks in duration).

Conclusion

In Argentina, RSV and influenza activity overlapped during the winter months. RSV season tended to begin prior to the influenza season, and showed more variation in start week by region. Influenza seasons tended to vary more in duration than RSV seasons.

Effect of time varying transmission rates on the coupled dynamics of epidemic and awareness over a multiplex network

A non-linear stochastic model is presented to study the effect of time variation of transmission rates on the co-evolution of epidemics and its corresponding awareness over a two layered multiplex network. In the model, the infection transmission rate of a given node in the epidemic layer depends upon its awareness probability in the awareness layer. Similarly, the infection information transmission rate of a node in the awareness layer depends upon its infection probability in the epidemic layer. The spread of disease resulting from physical contacts is described in terms of a Susceptible Infected Susceptible process over the epidemic layer and the spread of information about the disease outbreak is described in terms of an Unaware Aware Unaware process over the virtual interaction mediated awareness layer. The time variation of the transmission rates and the resulting co-evolution of these mutually competing processes are studied in terms of a network topology dependent parameter ( α ). Using a second order linear theory, it is shown that in the continuous time limit, the co-evolution of these processes can be described in terms of damped and driven harmonic oscillator equations. From the results of a Monte-Carlo simulation, it is shown that for a suitable choice of the parameter ( α ) , the two processes can either exhibit sustained oscillatory or damped dynamics. The damped dynamics corresponds to the endemic state. Furthermore, for the case of an endemic state, it is shown that the inclusion of the awareness layer significantly lowers the disease transmission rate and reduces the size of the epidemic. The infection probability of the nodes in the endemic state is found to have a dependence on both the transmission rates and on their absolute degrees in each of the network layers and on the relative differences between their degrees in the respective layers.

Transmission dynamics of influenza in two major cities of Uganda

In this paper, we report the epidemic characteristics of the three co-circulating influenza viruses (i.e., A/H1N1, A/H3N2, and B) in two tropical African cities-Kampala and Entebbe, Uganda-over an eight-year period (2008-2015). Using wavelet methods, we show that influenza epidemics recurred annually during the study period. In most months, two or more influenza viruses co-circulated at the same time. However, the epidemic timing differed by influenza (sub)type. Influenza A/H3N2 caused epidemics approximately every 2 years in both cities and tended to alternate with A/H1N1 or B. Influenza A/H1N1 and B produced smaller but more frequent epidemics and biennial epidemics of these two viruses tended to be synchronous. In addition, epidemics of A/H3N2 were more synchronized in the two cities (located ca.37 km apart) than that of A/H1N1 or influenza B.

Impact of weather factors on influenza hospitalization across different age groups in subtropical Hong Kong

Accumulating evidence demonstrates the significant influence of weather factors, especially temperature and humidity, on influenza seasonality. However, it is still unclear whether temperature variation within the same day, that is diurnal temperature range (DTR), is related to influenza seasonality. In addition, the different effects of weather factors on influenza seasonality across age groups have not been well documented in previous studies. Our study aims to explore the effects of DTR and humidity on influenza seasonality, and the differences in the association between weather factors and influenza seasonality among different age groups in Hong Kong, China. Generalized additive models were conducted to flexibly assess the impact of DTR, absolute humidity (vapor pressure, VP), and relative humidity on influenza seasonality in Hong Kong, China, from January 2012 to December 2016. Stratified analyses were performed to determine if the effects of weather factors differ across age groups (< 5, 5-9, 10-64, and > 64 years). The results suggested that DTR, absolute humidity, and relative humidity were significantly related to influenza seasonality in dry period (when VP is less than 20 mb), while no significant association was found in humid period (when VP is greater than 20 mb). The percentage changes of hospitalization rates due to influenza associated with per unit increase of weather factors in the very young children (age 0-4) and the elderly (age 65+) were higher than that in the adults (age 10-64). Diurnal temperature range is significantly associated with influenza seasonality in dry period, and the effects of weather factors differ across age groups in Hong Kong, China.

Dynamics of influenza in tropical Africa: Temperature, humidity, and co-circulating (sub)types

BACKGROUND

The association of influenza with meteorological variables in tropical climates remains controversial. Here, we investigate the impact of weather conditions on influenza in the tropics and factors that may contribute to this uncertainty.

METHODS

We computed the monthly viral positive rate for each of the 3 circulating influenza (sub)types (ie, A/H1N1, A/H3N2, and B) among patients presenting with influenza-like illness (ILI) or severe acute respiratory infections (SARI) in 2 Ugandan cities (Entebbe and Kampala). Using this measure as a proxy for influenza activity, we applied regression models to examine the impact of temperature, relative humidity, absolute humidity, and precipitation, as well as interactions among the 3 influenza viruses on the epidemic dynamics of each influenza (sub)type. A full analysis including all 4 weather variables was done for Entebbe during 2007-2015, and a partial analysis including only temperature and precipitation was done for both cities during 2008-2014.

RESULTS

For Entebbe, the associations with weather variables differed by influenza (sub)type; with adjustment for viral interactions, the models showed that precipitation and temperature were negatively correlated with A/H1N1 activity, but not for A/H3N2 or B. A mutually negative association between A/H3N2 and B activity was identified in both Entebbe and Kampala.

CONCLUSION

Our findings suggest that key interactions exist among influenza (sub)types at the population level in the tropics and that such interactions can modify the association of influenza activity with weather variables. Studies of the relationship between influenza and weather conditions should therefore determine and account for co-circulating influenza (sub)types.

Survival of the Enveloped Virus Phi6 in Droplets as a Function of Relative Humidity, Absolute Humidity, and Temperature

Infectious diseases caused by enveloped viruses, such as influenza, severe acute respiratory syndrome (SARS), and Middle East respiratory syndrome (MERS), cause thousands of deaths and billions of dollars of economic losses per year. Studies have found a relationship among temperature, humidity, and influenza virus incidence, transmission, or survival; however, there are contradictory claims about whether absolute humidity (AH) or relative humidity (RH) is most important in mediating virus infectivity. Using the enveloped bacteriophage Phi6, which has been suggested as a surrogate for influenza viruses and coronaviruses, we designed a study to discern whether AH, RH, or temperature is a better predictor of virus survival in droplets. Our results show that Phi6 survived best at high (>85%) and low (<60%) RHs, with a significant decrease in infectivity at mid-range RHs (∼60 to 85%). At an AH of less than 22 g · m-3, the loss in infectivity was less than 2 orders of magnitude; however, when the AH was greater than 22 g · m-3, the loss in infectivity was typically greater than 6 orders of magnitude. At a fixed RH of 75%, infectivity was very sensitive to temperature, decreasing two orders of magnitude between 19°C and 25°C. We used random forest modeling to identify the best environmental predictors for modulating virus infectivity. The model explained 83% of variation in Phi6 infectivity and suggested that RH is the most important factor in controlling virus infectivity in droplets. This research provides novel information about the complex interplay between temperature, humidity, and the survival of viruses in droplets.IMPORTANCE Enveloped viruses are responsible for a number of infectious diseases resulting in thousands of deaths and billions of dollars of economic losses per year in the United States. There has been a lively debate in the literature over whether absolute humidity (AH) or relative humidity (RH) modulates virus infectivity. We designed a controlled study and used advanced statistical modeling techniques specifically to address this question. By providing an improved understanding of the relationship between environmental conditions and virus infectivity, our work will ultimately lead to improved strategies for predicting and controlling disease transmission.

Duration of Stay of Patients with Community-Acquired Pneumonia in Influenza Season

OBJECTIVES

There is a seasonal variation in the incidence of some infectious diseases. We analyzed the impact of influenza season (IS) on duration of stay (DOS) and some other characteristics of patients with community-acquired pneumonia (CAP).

MATERIALS AND METHODS

In our retrospective cohort study, we analyzed data of 369 patients with CAP.

RESULTS

The mean patient age was 65.5±16.69 years, and 267 (72.4%) patients were male. There was no difference between patients with CAP admitted to hospital and intensive care unit during IS and non-influenza season (NIS) with respect to age, mortality, and DOS. There was no difference in leukocyte and neutrophil counts, C-reactive protein level, and erythrocyte sedimentation rate in different seasons. Although most comorbid disease rates were similar, only cancer, especially lung cancer, was more prevalent in NIS. Bilateral CAP confirmed using thorax computed tomography was more frequent in IS.

CONCLUSION

Although more patients with bilateral pneumonias were hospitalized in IS, DOS was not different between IS and NIS.

Analysis of multi-level spatial data reveals strong synchrony in seasonal influenza epidemics across Norway, Sweden, and Denmark

Population structure, spatial diffusion, and climatic conditions mediate the spatiotemporal spread of seasonal influenza in temperate regions. However, much of our knowledge of these dynamics stems from a few well-studied countries, such as the United States (US), and the extent to which this applies in different demographic and climatic environments is not fully understood. Using novel data from Norway, Sweden, and Denmark, we applied wavelet analysis and non-parametric spatial statistics to explore the spatiotemporal dynamics of influenza transmission at regional and international scales. We found the timing and amplitude of epidemics were highly synchronized both within and between countries, despite the geographical isolation of many areas in our study. Within Norway, this synchrony was most strongly modulated by population size, confirming previous findings that hierarchical spread between larger populations underlies seasonal influenza dynamics at regional levels. However, we found no such association when comparing across countries, suggesting that other factors become important at the international scale. Finally, to frame our results within a wider global context, we compared our findings from Norway to those from the US. After correcting for differences in geographic scale, we unexpectedly found higher levels of synchrony in Norway, despite its smaller population size. We hypothesize that this greater synchrony may be driven by more favorable and spatially uniform climatic conditions, although there are other likely factors we were unable to consider (such as reduced variation in school term times and differences in population movements). Overall, our results highlight the importance of comparing influenza spread at different spatial scales and across diverse geographic regions in order to better understand the complex mechanisms underlying disease dynamics.

A paradox of epidemics between the state and parameter spaces

It is recently revealed from amounts of real data of recurrent epidemics that there is a phenomenon of hysteresis loop in the state space. To understand it, an indirect investigation from the parameter space has been given to qualitatively explain its mechanism but a more convincing study to quantitatively explain the phenomenon directly from the state space is still missing. We here study this phenomenon directly from the state space and find that there is a positive correlation between the size of outbreak and the size of hysteresis loop, implying that the hysteresis is a nature feature of epidemic outbreak in real case. Moreover, we surprisingly find a paradox on the dependence of the size of hysteresis loop on the two parameters of the infectious rate increment and the transient time, i.e. contradictory behaviors between the two spaces, when the evolutionary time of epidemics is long enough. That is, with the increase of the infectious rate increment, the size of hysteresis loop will decrease in the state space but increase in the parameter space. While with the increase of the transient time, the size of hysteresis loop will increase in the state space but decrease in the parameter space. Furthermore, we find that this paradox will disappear when the evolutionary time of epidemics is limited in a fixed period. Some theoretical analysis are presented to both the paradox and other numerical results.

Independent and interactive effects of ambient temperature and absolute humidity on the risks of avian influenza A(H7N9) infection in China

The emergence of avian influenza A(H7N9) virus poses a pandemic threat to human beings. It was proposed that meteorological factors might be important environmental factors favoring the occurrence of H7N9 infection, but evidence is still inadequate. In this study, we aimed to investigate the independent and interactive effects of ambient temperature (TM) and absolute humidity (AH) on H7N9 infection risks in China. The individual information of all reported H7N9 cases and daily meteorological data in five provinces/municipality (Zhejiang, Jiangsu, Shanghai, Fujian, and Guangdong) in China during 2013-2016 were collected. We employed a case-crossover study design, in which the 7-10days before the onset date of each H7N9 case was defined as the hazard period, and 4weeks before the hazard period was taken as the control period. The average levels of meteorological factors were calculated during the hazard and control periods. A Cox regression model was used to estimate the independent and interactive effects of TM and vapor pressure (VP), an indicator of AH, on H7N9 infection risks. A total of 738 H7N9 cases were included in the present study. Significantly nonlinear negative associations of TM and VP with H7N9 infection risks were observed in all cases, and in cases from northern and southern regions. There were significant interactive effects between TM and VP on H7N9 infection risks, and the risks of H7N9 infection were higher in cold-dry days than other days. We further observed different risky windows of H7N9 infection in the northern (TM: 0-18°C, VP: 313mb) and southern areas (TM: 7-21°C, VP: 3-17mb). We concluded that ambient temperature and absolute humidity had significant independent and interactive effects on H7N9 infection risks in China, and the risks of H7N9 infection were higher in cold-dry days. The risky windows of H7N9 infection were different in the northern and southern areas.

Influenza virus: 16 years' experience of clinical epidemiologic patterns and associated infection factors in hospitalized children in Argentina

Background

Influenza is an important cause of acute lower respiratory tract infection (aLRTI), hospitalization, and mortality in children. This study aimed to describe the clinical and epidemiologic patterns and infection factors associated with influenza, and compare case features of influenza A and B.

Methods

In a prospective, cross-sectional study, patients admitted for aLRTI, between 2000 and 2015, were tested for respiratory syncytial virus, adenovirus, influenza, or parainfluenza, and confirmed by fluorescent antibody (FA) or real-time polymerase chain reaction (RT-PCR) assay of nasopharyngeal aspirates.

Results

Of 14,044 patients, 37.7% (5290) had FA- or RT-PCR-confirmed samples that identified influenza in 2.8% (394/14,044; 91.4% [360] influenza A, 8.6% [34] influenza B) of cases. Influenza frequency followed a seasonal epidemic pattern (May–July, the lowest average temperature months). The median age of cases was 12 months (interquartile range: 6–21 months); 56.1% (221/394) of cases were male. Consolidated pneumonia was the most frequent clinical presentation (56.9%; 224/394). Roughly half (49.7%; 196/394) of all cases had previous respiratory admissions; 9.4% (37/394) were re-admissions; 61.5% (241/392) had comorbidities; 26.2% (102/389) had complications; 7.8% (30/384) had nosocomial infections. The average case fatality rate was 2.1% (8/389). Chronic neurologic disease was significantly higher in influenza B cases compared to influenza A cases (p = 0.030). The independent predictors for influenza were: age ≥6 months, odds ratio (OR): 1.88 (95% confidence interval [CI]: 1.44–2.45); p<0.001; presence of chronic neurologic disease, OR: 1.48 (95% CI: 1.01–2.17); p = 0.041; previous respiratory admissions, OR: 1.71 (95% CI: 1.36–2.14); p<0.001; re-admissions, OR: 1.71 (95% CI: 1.17–2.51); p = 0.006; clinical pneumonia, OR: 1.50 (95% CI: 1.21–1.87); p<0.001; immunodeficiency, OR: 1.87 (95% CI: 1.15–3.05); p = 0.011; cystic fibrosis, OR: 4.42 (95% CI: 1.29–15.14); p = 0.018.

Conclusion

Influenza showed an epidemic seasonal pattern (May–July), with higher risk in children ≥6 months, or with pneumonia, previous respiratory admissions, or certain comorbidities.

Deploying digital health data to optimize influenza surveillance at national and local scales

The surveillance of influenza activity is critical to early detection of epidemics and pandemics and the design of disease control strategies. Case reporting through a voluntary network of sentinel physicians is a commonly used method of passive surveillance for monitoring rates of influenza-like illness (ILI) worldwide. Despite its ubiquity, little attention has been given to the processes underlying the observation, collection, and spatial aggregation of sentinel surveillance data, and its subsequent effects on epidemiological understanding. We harnessed the high specificity of diagnosis codes in medical claims from a database that represented 2.5 billion visits from upwards of 120,000 United States healthcare providers each year. Among influenza seasons from 2002-2009 and the 2009 pandemic, we simulated limitations of sentinel surveillance systems such as low coverage and coarse spatial resolution, and performed Bayesian inference to probe the robustness of ecological inference and spatial prediction of disease burden. Our models suggest that a number of socio-environmental factors, in addition to local population interactions, state-specific health policies, as well as sampling effort may be responsible for the spatial patterns in U.S. sentinel ILI surveillance. In addition, we find that biases related to spatial aggregation were accentuated among areas with more heterogeneous disease risk, and sentinel systems designed with fixed reporting locations across seasons provided robust inference and prediction. With the growing availability of health-associated big data worldwide, our results suggest mechanisms for optimizing digital data streams to complement traditional surveillance in developed settings and enhance surveillance opportunities in developing countries.

Influenza contributes substantially to global morbidity and mortality each year, and epidemiological surveillance for influenza is typically conducted by sentinel physicians and health care providers recruited to report cases of influenza-like illness. While population coverage and representativeness, and geographic distribution are considered during sentinel provider recruitment, systems cannot always achieve these standards due to the administrative burdens of data collection. We present spatial estimates of influenza disease burden across United States counties by leveraging the volume and fine spatial resolution of medical claims data, and existing socio-environmental hypotheses about the determinants of influenza disease disease burden. Using medical claims as a testbed, this study adds to literature on the optimization of surveillance system design by considering conditions of limited reporting and spatial aggregation. We highlight the importance of considering sampling biases and reporting locations when interpreting surveillance data, and suggest that local mobility and regional policies may be critical to understanding the spatial distribution of reported influenza-like illness.

Global influenza seasonality to inform country-level vaccine programs: An analysis of WHO FluNet influenza surveillance data between 2011 and 2016

By analyzing publicly available surveillance data from 2011-2016, we produced country-specific estimates of seasonal influenza activity for 118 countries in the six World Health Organization regions. Overall, the average country influenza activity period was 4.7 months. Our analysis characterized 100 countries (85%) with one influenza peak season, 13 (11%) with two influenza peak seasons, and five (4%) with year-round influenza activity. Surveillance data were limited for many countries. These data provide national estimates of influenza activity, which may guide planning for influenza vaccination implementation, program timing and duration, and policy development.

Physico-chemical characteristics of evaporating respiratory fluid droplets

The detailed physico-chemical characteristics of respiratory droplets in ambient air, where they are subject to evaporation, are poorly understood. Changes in the concentration and phase of major components in a droplet-salt (NaCl), protein (mucin) and surfactant (dipalmitoylphosphatidylcholine)-may affect the viability of any pathogens contained within it and thus may affect the efficiency of transmission of infectious disease by droplets and aerosols. The objective of this study is to investigate the effect of relative humidity (RH) on the physico-chemical characteristics of evaporating droplets of model respiratory fluids. We labelled these components in model respiratory fluids and observed evaporating droplets suspended on a superhydrophobic surface using optical and fluorescence microscopy. When exposed to continuously decreasing RH, droplets of different model respiratory fluids assumed different morphologies. Loss of water induced phase separation as well as indication of a decrease in pH. The presence of surfactant inhibited the rapid rehydration of the non-volatile components. An enveloped virus, ϕ6, that has been proposed as a surrogate for influenza virus appeared to be homogeneously distributed throughout the dried droplet. We hypothesize that the increasing acidity and salinity in evaporating respiratory droplets may affect the structure of the virus, although at low enough RH, crystallization of the droplet components may eliminate their harmful effects.

Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology

Seasonal influenza is controlled through vaccination campaigns. Evolution of influenza virus antigens means that vaccines must be updated to match novel strains, and vaccine effectiveness depends on the ability of scientists to predict nearly a year in advance which influenza variants will dominate in upcoming seasons. In this review, we highlight a promising new surveillance tool: predictive models. Based on data-sharing and close collaboration between the World Health Organization and academic scientists, these models use surveillance data to make quantitative predictions regarding influenza evolution. Predictive models demonstrate the potential of applied evolutionary biology to improve public health and disease control. We review the state of influenza predictive modeling and discuss next steps and recommendations to ensure that these models deliver upon their considerable biomedical promise.

Prediction of influenza peaks in Russian cities: Comparing the accuracy of two SEIR models

This paper is dedicated to the application of two types of SEIR models to the influenza outbreak peak prediction in Russian cities. The first one is a continuous SEIR model described by a system of ordinary differential equations. The second one is a discrete model formulated as a set of difference equations, which was used in the Baroyan-Rvachev modeling framework for the influenza outbreak prediction in the Soviet Union. The outbreak peak day and height predictions were performed by calibrating both models to varied-size samples of long-term data on ARI incidence in Moscow, Saint Petersburg, and Novosibirsk. The accuracy of the modeling predictions on incomplete data was compared with a number of other peak forecasting methods tested on the same dataset. The drawbacks of the described prediction approach and possible ways to overcome them are discussed.

Epidemiologic and Spatiotemporal Characterization of Influenza and Severe Acute Respiratory Infection in Uganda, 2010-2015

RATIONALE

Little is known about the epidemiology of severe acute respiratory infection (SARI) or influenza in sub-Saharan Africa. Characterization of influenza transmission dynamics and risk factors for severe disease and mortality is critical to inform prevention and mitigation strategies.

OBJECTIVES

To characterize the epidemiology and transmission dynamics of influenza and risk factors for influenza-associated severe respiratory infection in Uganda.

METHODS

Clinicians at 12 sentinel surveillance sites prospectively collected clinical data and upper respiratory tract samples from consecutive patients who met criteria for SARI and influenza-like illness (ILI). Samples were tested for influenza A and B viruses using real-time reverse transcription-polymerase chain reaction. Spatial and spatiotemporal cluster modeling was performed to identify loci of increased influenza transmission. Morbidity and mortality were assessed through chart review in a defined subset of patients. Univariable and multivariable analyses were used to identify risk factors for severe respiratory infection, prolonged hospitalization, and in-hospital mortality.

MEASUREMENTS AND MAIN RESULTS

From October 2010 to June 2015, 9,978 patients met case definitions for SARI and ILI and had samples tested for influenza A and B. Of the 9,978 patient samples tested, 1,113 (11.2%) were positive for influenza. Among 6,057 patients with ILI, 778 samples (12.8%) were positive, and among 3,921 patients with SARI, 335 samples (8.5%) were positive. Significant clustering of influenza cases was observed in urban and periurban areas and during rainy seasons. Among 1,405 cases of SARI with available outcome data, in-hospital mortality was 1.6%. Infection with the 2009 pandemic A/H1N1 subtype and prolonged time to presentation were independently associated with SARI among influenza cases.

CONCLUSIONS

Influenza is associated with a substantial proportion of acute respiratory infection in Uganda. As influenza vaccination programs are developed in East Africa, timing campaigns to confer protection during rainy seasons should be considered, particularly among high-risk urban populations.

The effects of synoptic weather on influenza infection incidences: a retrospective study utilizing digital disease surveillance

The environmental drivers and mechanisms of influenza dynamics remain unclear. The recent development of influenza surveillance--particularly the emergence of digital epidemiology--provides an opportunity to further understand this puzzle as an area within applied human biometeorology. This paper investigates the short-term weather effects on human influenza activity at a synoptic scale during cold seasons. Using 10 years (2005-2014) of municipal level influenza surveillance data (an adjustment of the Google Flu Trends estimation from the Centers for Disease Control's virologic surveillance data) and daily spatial synoptic classification weather types, we explore and compare the effects of weather exposure on the influenza infection incidences in 79 cities across the USA. We find that during the cold seasons the presence of the polar [i.e., dry polar (DP) and moist polar (MP)] weather types is significantly associated with increasing influenza likelihood in 62 and 68% of the studied cities, respectively, while the presence of tropical [i.e., dry tropical (DT) and moist tropical (MT)] weather types is associated with a significantly decreasing occurrence of influenza in 56 and 43% of the cities, respectively. The MP and the DP weather types exhibit similar close positive correlations with influenza infection incidences, indicating that both cold-dry and cold-moist air provide favorable conditions for the occurrence of influenza in the cold seasons. Additionally, when tropical weather types are present, the humid (MT) and the dry (DT) weather types have similar strong impacts to inhibit the occurrence of influenza. These findings suggest that temperature is a more dominating atmospheric factor than moisture that impacts the occurrences of influenza in cold seasons.

High-resolution Temporal Representations of Alcohol and Tobacco Behaviors from Social Media Data

Understanding tobacco- and alcohol-related behavioral patterns is critical for uncovering risk factors and potentially designing targeted social computing intervention systems. Given that we make choices multiple times per day, hourly and daily patterns are critical for better understanding behaviors. Here, we combine natural language processing, machine learning and time series analyses to assess Twitter activity specifically related to alcohol and tobacco consumption and their sub-daily, daily and weekly cycles. Twitter self-reports of alcohol and tobacco use are compared to other data streams available at similar temporal resolution. We assess if discussion of drinking by inferred underage versus legal age people or discussion of use of different types of tobacco products can be differentiated using these temporal patterns. We find that time and frequency domain representations of behaviors on social media can provide meaningful and unique insights, and we discuss the types of behaviors for which the approach may be most useful.

Associations between meteorological parameters and influenza activity in a subtropical country: Case of five sentinel sites in Yaoundé-Cameroon

Influenza is associated with highly contagious respiratory infections. Previous research has found that influenza transmission is often associated with climate variables especially in temperate regions. This study was performed in order to fill the gap of knowledge regarding the relationship between incidence of influenza and three meteorological parameters (temperature, rainfall and humidity) in a tropical setting. This was a retrospective study performed in Yaoundé-Cameroon from January 2009 to November 2015. Weekly proportions of confirmed influenza cases from five sentinel sites were considered as dependent variables, whereas weekly values of mean temperature, average relative humidity and accumulated rainfall were considered as independent variables. A univariate linear regression model was used in determining associations between influenza activity and weather covariates. A time-series method was used to predict on future values of influenza activity. The data was divided into 2 parts; the first 71 months were used to calibrate the model, and the last 12 months to test for prediction. Overall, there were 1173 confirmed infections with influenza virus. Linear regression analysis showed that there was no statistically significant association observed between influenza activity and weather variables. Very weak relationships (-0.1 < r < 0.1) were observed. Three prediction models were obtained for the different viral types (overall positive, Influenza A and Influenza B). Model 1 (overall influenza) and model 2 (influenza A) fitted well during the estimation period; however, they did not succeed to make good forecasts for predictions. Accumulated rainfall was the only external covariate that enabled good fit of both models. Based on the stationary R², 29.5% and 41.1% of the variation in the series can be explained by model 1 and 2, respectively. This study laid more emphasis on the fact that influenza in Cameroon is characterized by year-round activity. The meteorological variables selected in this study did not enable good forecast of future influenza activity and certainly acted as proxies to other factors not considered, such as, UV radiation, absolute humidity, air quality and wind.