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Background Information on seasonal malaria transmission is required for the development of targeted intervention programmes in malaria-endemic countries. This study aimed to determine the epidemic periods of malaria by health district and target group in Togo. Methods Monthly data of confirmed malaria cases from 2013 to 2017 were analysed in this study. Data were routinely collected by the Togo National Malaria Control Programme (NMCP). They were aggregated by health district and target group (children < 5 years old, children ≥ 5 years old and adults, and pregnant women). Estimates of excess malaria cases compared to January were obtained through generalised additive models. The number of epidemic periods, the number of months with an excess of cases, the months with an excess of cases, the relative percentage increase of cases in the first month with an excess of cases, and the maximum relative percentage increase of cases and the corresponding month were described for each health district and target group. Results A total of 5,522,650 confirmed malaria cases were reported from 2013 to 2017 in Togo. Children < 5 years old, children ≥ 5 years old and adults, and pregnant women represented 36.6%, 58.5% and 4.9% of the confirmed malaria cases, respectively. A time lag of at least one month was generally observed between the onset of precipitation and the start of the epidemic periods in all three target groups. In the health districts of the Savanes region, the epidemic periods started later in the year and had a greater relative increase in malaria cases compared to January than in the health districts of the other regions. In contrast, in the health districts of the Maritime and Lome-commune regions, the epidemic periods were generally short or undetectable. Conclusions This study suggests that malaria control interventions should be tailored to local transmission, considering the onset and duration of epidemic periods at the district level. These results can be used to adapt the distribution of seasonal malaria chemoprevention in children < 5 years old, and then used for malaria risk stratification in addition to other data.

Seasonal epidemics erupt commonly in nature and are driven by numerous mechanisms. Here, we suggest a new mechanism that could determine the size and timing of seasonal epidemics: rearing environment changes the performance of parasites. This mechanism arises when the environmental conditions in which a parasite is produced impact its performance—independently from the current environment. To illustrate the potential for “rearing effects”, we show how temperature influences infection risk (transmission rate) in a Daphnia-fungus disease system through both parasite rearing temperature and infection temperature. During autumnal epidemics, zooplankton hosts contact (eat) fungal parasites (spores) reared in a gradually cooling environment. To delineate the effect of rearing temperature from temperature at exposure and infection, we used lab experiments to parameterize a mechanistic model of transmission rate. We also evaluated the rearing effect using spores collected from epidemics in cooling lakes. We found that fungal spores were more infectious when reared at warmer temperatures (in the lab and in two of three lakes). Additionally, the exposure (foraging) rate of hosts increased with warmer infection temperatures. Thus, both mechanisms cause transmission rate to drop as temperature decreases over the autumnal epidemic season (from summer to winter). Simulations show how these temperature-driven changes in transmission rate can induce waning of epidemics as lakes cool. Furthermore, via thermally dependent transmission, variation in environmental cooling patterns can alter the size and shape of epidemics. Thus, the thermal environment drives seasonal epidemics through effects on hosts (exposure rate) and the infectivity of parasites (a rearing effect). Presently, the generality of parasite rearing effects remains unknown. Our results suggest that they may provide an important but underappreciated mechanism linking temperature to the seasonality of epidemics.

Background Forecasting models predicting trends in hospitalization rates have the potential to inform hospital management during seasonal epidemics of respiratory diseases and the associated surges caused by acute hospital admissions. Hospital bed requirements for elective surgery could be better planned if it were possible to foresee upcoming peaks in severe respiratory illness admissions. Forecasting models can also guide the use of intervention strategies to decrease the spread of respiratory pathogens and thus prevent local health system overload. In this study, we explore the capability of forecasting models to predict the number of hospital admissions in Auckland, New Zealand, within a three-week time horizon. Furthermore, we evaluate probabilistic forecasts and the impact on model performance when integrating laboratory data describing the circulation of respiratory viruses. Methods The dataset used for this exploration results from active hospital surveillance, in which the World Health Organization Severe Acute Respiratory Infection (SARI) case definition was consistently used. This research nurse-led surveillance has been implemented in two public hospitals in Auckland and provides a systematic laboratory testing of SARI patients for nine respiratory viruses, including influenza, respiratory syncytial virus, and rhinovirus. The forecasting strategies used comprise automatic machine learning, one of the most recent generative pre-trained transformers, and established artificial neural network algorithms capable of univariate and multivariate forecasting. Results We found that machine learning models compute more accurate forecasts in comparison to naïve seasonal models. Furthermore, we analyzed the impact of reducing the temporal resolution of forecasts, which decreased the model error of point forecasts and made probabilistic forecasting more reliable. An additional analysis that used the laboratory data revealed strong season-to-season variations in the incidence of respiratory viruses and how this correlates with total hospitalization cases. These variations could explain why it was not possible to improve forecasts by integrating this data. Conclusions Active SARI surveillance and consistent data collection over time enable these data to be used to predict hospital bed utilization. These findings show the potential of machine learning as support for informing systems for proactive hospital management.

Background Unlike influenza viruses, little is known about the prevalence and seasonality of other respiratory viruses because laboratory surveillance for non-influenza respiratory viruses is not well developed or supported in China and other resource-limited countries. We studied the interference between seasonal epidemics of influenza viruses and five other common viruses that cause respiratory illnesses in Hong Kong from 2014 to 2017. Methods The weekly laboratory-confirmed positive rates of each virus were analyzed from 2014 to 2017 in Hong Kong to describe the epidemiological trends and interference between influenza viruses, respiratory syncytial virus (RSV), parainfluenza virus (PIV), adenovirus, enterovirus and rhinovirus. A sinusoidal model was established to estimate the peak timing of each virus by phase angle parameters. Results Seasonal features of the influenza viruses, PIV, enterovirus and adenovirus were obvious, whereas annual peaks of RSV and rhinovirus were not observed. The incidence of the influenza viruses usually peaked in February and July, and the summer peaks in July were generally caused by the H3 subtype of influenza A alone. When influenza viruses were active, other viruses tended to have a low level of activity. The peaks of the influenza viruses were not synchronized. An epidemic of rhinovirus tended to shift the subsequent epidemics of the other viruses. Conclusion The evidence from recent surveillance data in Hong Kong suggests that viral interference during the epidemics of influenza viruses and other common respiratory viruses might affect the timing and duration of subsequent epidemics of a certain or several viruses.

Background: During seasonal influenza activity, circulation of other respiratory viruses (ORVs) may contribute to the increased disease burden that is attributed to influenza without laboratory confirmation. The objective of this study was to characterize and evaluate the magnitude of this contribution over 12 seasons of influenza using the Acute Respiratory Infection Sentinel Surveillance system in Catalonia (PIDIRAC). Methods: A retrospective descriptive study of isolations from respiratory samples obtained by the sentinel surveillance network of physicians was carried out from 2008 to 2020 in Catalonia, Spain. Information was collected on demographic variables (age, sex), influenza vaccination status, epidemic activity weeks each season, and influenza laboratory confirmation. Results: A total of 12,690 samples were collected, with 46% (5831) collected during peak influenza seasonal epidemic activity. In total, 49.6% of the sampled participants were male and 51.1% were aged <15 years. Of these, 73.7% (4298) of samples were positive for at least one respiratory virus; 79.7% (3425 samples) were positive for the influenza virus (IV), with 3067 samples positive for one IV type, 8 samples showing coinfection with two types of IV, and 350 showing coinfection of IV with more than one virus. The distribution of influenza viruses was 64.2% IVA, 35.2% IVB, and 0.1% IVC. Of the other respiratory viruses identified, there was a high proportion of human rhinovirus (32.3%), followed by human adenovirus (24.3%) and respiratory syncytial virus (18; 7%). Four percent were coinfected with two or more viruses other than influenza. The distribution of coinfections with ORVs and influenza by age groups presents a significant difference in proportions for 0–4, 5–14, 15–64 and >64 (21.5%, 10.8%, 8.2% and 7.6%: p < 0.001). A lower ORVs coinfection ratio was observed in the influenza-vaccinated population (11.9% vs. 17.4% OR: 0.64 IC 95% 0.36–1.14). Conclusions: During the weeks of seasonal influenza epidemic activity, other respiratory viruses contribute substantially, either individually or through the coinfection of two or more viruses, to the morbidity attributed to influenza viruses as influenza-like illness (ILI). The contribution of these viruses is especially significant in the pediatric and elderly population. Identifying the epidemiology of most clinically relevant respiratory viruses will aid the development of models of infection and allow for the development of targeted treatments, particularly for populations most vulnerable to respiratory viruses-induced diseases.

We consider a SIRS model with time dependent transmission rate. We assume time dependent vaccination which confers the same immunity as natural infection. We study two types of vaccination strategies: (i) optimal vaccination, in the sense that it minimizes the effort of vaccination in the set of vaccination strategies for which, for any sufficiently small perturbation of the disease free state, the number of infectious individuals is monotonically decreasing; (ii) Nash-equilibria strategies where all individuals simultaneously minimize the joint risk of vaccination versus the risk of the disease. The former case corresponds to an optimal solution for mandatory vaccinations, while the second corresponds to the equilibrium to be expected if vaccination is fully voluntary. We are able to show the existence of both optimal and Nash strategies in a general setting. In general, these strategies will not be functions but Radon measures. For specific forms of the transmission rate, we provide explicit formulas for the optimal and the Nash vaccination strategies.

To evaluate the impact of an influenza vaccination (IV) coverage (IVC) in a vaccination campaign of an Emergency Department (EDVC) and its impact on ED time interval quality indicators. We conducted a 4 year observational study, with an intervention during the 4th year. IVC was calculated during pre-and early-epidemic periods. During the final period, a 12 weeks EDVC was implemented. Physicians and nurses were trained and sensitized in the importance of vaccination, and their role in the prevention of severe forms of influenza was reinforced. The vaccine was proposed by physicians and nurses, and delivered by them. Repeated measures ANOVA is a validated method for related not independent groups (https://statistics.laerd.com/statistical-guides/repeated-measures-anova-statistical-guide.php). Overall, IVC was 987/3191 (30.9%) with an increasing trend from 28.8 to 33.2%. In the fourth period, out of 868 patients identified with IV indication, 288 had already been vaccinated (IVC 33.2%). After excluding patients presenting criteria of exclusion, IV was proposed to 475 patients: 317 (66.7%) accepted. The vaccination rate after patient’s acceptance was 89.6% (288/317). At the end of the EDVC, influenza vaccination coverage was 572 (284 + 288)/868 (65.9%). The delay between arrival at the ED and seeing the triage nurse and physician as well as the overall ED length of stay were not modified during the study period and before and during EDVC. EDVC effectively doubled the influenza vaccination coverage, without modifying ED time interval quality indicators.

To investigate the hand, foot and mouth disease (HFMD) epidemic in Zhenjiang, China from 2008 to 2016. A total of 37,202 HFMD cases were investigated and 3707 nasopharyngeal swabs were detected for enterovirus RNA using RT-quantitative PCR. We first reported a mixed pattern of HFMD seasonal epidemic with a combination of single-peak and two-peak patterns in alternate years, and the occurrence of sporadic and epidemic outbreaks of HFMD in kindergartens in Zhenjiang. Children younger than 4 years of age were highly vulnerable to HFMD, and home children and boys had higher risk to develop severe HFMD than nursery children and girls, respectively. Among tested samples, 1709 (46.1%) were detected as enterovirus RNA positive. This study first presents the dynamic of the HFMD epidemic in Zhenjiang from 2008 to 2016.

The resource quality of the host has been shown to affect parasite transmission success, prevalence, and virulence. Seasonal availability of environmental nutrients alters density and stoichiometric quality (carbon-nutrient ratios) of both producers and consumers, suggesting that nutrient availability may drive fluctuations in parasite prevalence patterns observed in nature. We examined the interactions between the population dynamics of a keystone herbivore, Daphnia , and its parasites, and their associations with water nutrient concentrations, resource quantity and quality, and other environmental variables (temperature, pH, oxygen concentration) in a small lake, using general linear models. We found that the prevalence of two gut endoparasites was positively related to food source and quality as well as nitrogen content of Daphnia , whereas the prevalence of an epibiont and overall parasite species richness was negatively related to phosphorus content of Daphnia . When only endoparasite species richness was considered, no connections to nutrients were found. Daphnia density was not connected to parasites, but we found interactions between Daphnia fecundity and parasite prevalence. Overall, our results suggest that environmental nutrient concentrations and stoichiometric quality of the host have the potential to affect seasonality in parasite epidemics, but the connections between environmental carbon : nutrient ratios and parasite prevalence patterns are diverse and species specific.

Background This study aims to quantify the excess of sickness absenteeism among healthcare workers (HCWs), to estimate the impact of a severe versus moderate influenza season and to determine whether the vaccination rates are associated with reduced sickness absence. Methods We investigated the excess absenteeism that occurred in a large Italian hospital, 5300 HCWs, during the severe influenza season of 2017/2018 and compared it with three moderate flu seasons (2010/2013). Data on influenza vaccinations and absenteeism were obtained from the hospital's databases. The data were split into two periods: the epidemic, from 42 to 17 weeks, and non‐epidemic, defined as 18 to 41 weeks, which was used as the baseline. We stratified the absenteeism among HCWs in multiple variables. Results Our study showed an increased absenteeism among HCWs during the epidemic period of severe season in comparison with non‐epidemic periods, the absolute increase correlated with a relative increase of 70% (from 4.05 to 6.68 days/person). Vaccinated HCWs had less excess of absenteeism in comparison with non‐vaccinated HCWs (1.74 vs 2.71 days/person). The comparison with the moderate seasons showed a stronger impact on HCW sick absenteeism in the severe season (+0.747days/person, P = .03), especially among nurses and HCWs in contact with patients (+1.53 P < .01; +1.19 P < .01). Conclusions In conclusion, a severe influenza epidemic has greater impacts on the absenteeism among HCWs than a moderate one. Although at a low rate, a positive effect of vaccination on absenteeism is present, it may support healthcare facilities to recommend vaccinations for their workers.