Seasonal Infectious Disease Research Articles

Mortality Associated with Infectious Diseases in Dutch Nursing Homes

BackgroundAlthough older people in nursing homes have a larger susceptibility to infectious diseases, the extent to which infectious diseases contribute to their mortality is unknown. Therefore, we quantified the associations between seasonal infectious diseases and all-cause mortality in Dutch nursing homes. MethodsWe analyzed time series (January 2009 to December 2021) of the weekly sentinel surveillance of infectious diseases in Dutch nursing homes. A generalized linear model with binomial distribution and identity link was used to associate the proportion of all-cause mortality with the incidence of infections: COVID-19 (2020–2021), gastroenteritis (GE), influenza-like illness (ILI), and lower respiratory tract infections (LRTIs) at 0- to 4-week time-lags (mortality incidence at 0 to 4 weeks after infections incidence). ResultsOver 13 years, 81 nursing homes participated, with 20 to 35 homes each year (mean: 26). A total of 11,555 all-cause deaths occurred over 1,864,667 resident weeks, averaging a mortality incidence of 6.2 per 1000 resident weeks. All 4 tested infectious diseases exhibited a significant association with all-cause mortality in nursing homes (P ≤ .01). Collectively, these infectious diseases were associated with 18.6% (95% CI, 17.8%–19.3%) of all deaths. The association between mortality and ILI was significant in 7 of 12 ILI seasons. Yearly mortality associated with the specific infectious diseases was as follows: LRTI (10.2%; 95% CI, 9.6%–10.8%), ILI (8.2%; 95% CI, 7.5%–8.9%) (over the 7 of 12 significant seasons), COVID-19 (6.5%; 95% CI, 5.4%–7.7%) (over 2019–2021 as there was no previous SARS-CoV-2 circulation), and GE (2.3%; 95% CI, 2.0%–2.5%). Conclusion and ImplicationsIn nursing homes, the occurrence of seasonal respiratory and gastrointestinal infections is associated with nearly one-fifth of all-cause deaths. Although infection prevention and surveillance may already be performed in some nursing homes, it is vital to implement, and enhance targeted strategies like (hand) hygiene protocols, environmental cleaning practices, reducing droplet and aerosol transmission, and vaccination to effectively address specific infections.

Application of improved harmonic Poisson segmented regression model in evaluating the effectiveness of Kala-Azar intervention in Yangquan City, China.

The Centre for Disease Control and Prevention in Yangquan, China, has taken a series of preventive and control measures in response to the increasing trend of Kala-Azar. In response, we propose a new model to more scientifically evaluate the effectiveness of these interventions. We obtained the incidence data of Kala-Azar from 2017 to 2021 from the Centre for Disease Control and Prevention (CDC) in Yangquan. We constructed Poisson segmented regression model, harmonic Poisson segmental regression model, and improved harmonic Poisson segmented regression model, and used the three models to explain the intervention effect, respectively. Finally, we selected the optimal model by comparing the fitting effects of the three models. The primary analysis showed an underlying upward trend of Kala-Azar before intervention [incidence rate ratio (IRR): 1.045, 95% confidence interval (CI): 1.027-1.063, p < 0.001]. In terms of long-term effects, the rise of Kala-Azar slowed down significantly after the intervention (IRR:0.960, 95%CI:0.927-0.995, p = 0.026), and the risk of Kala-Azar increased by 0.3% for each additional month after intervention (β1 + β3 = 0.003, IRR = 1.003). The results of the model fitting effect showed that the improved harmonic Poisson segmental regression model had the best fitting effect, and the values of MSE, MAE, and RMSE were the lowest, which were 0.017, 0.101, and 0.130, respectively. In the long term, the intervention measures taken by the Yangquan CDC can well curb the upward trend of Kala-Azar. The improved harmonic Poisson segmented regression model has higher fitting performance, which can provide a certain scientific reference for the evaluation of the intervention effect of seasonal infectious diseases.

Clinico-epidemiological features of influenza in children in the 2023-2024 season

Seasonal respiratory illnesses are the main cause of morbidity during the cold season of the year both in adults and especially in children due to clinical and epidemiological peculiarities. Influenza is one of the most important seasonal diseases causing numerous illnesses every year among the population at risk (young children, adults with comorbidities, and the elderly). In this paper we present the clinico-epidemiological features of influenza in children admitted to the National Institute of Infectious Diseases “Prof. Dr. Matei Bals” between October 2023 and March 2024. During the analysed period we registered 739 cases of influenza in children, which represents 23.7% of the total admissions in the same period. Cases of influenza A (68.3 %) predominated, compared to type B (18.8 %) and unspecified (12.8 %). The majority of cases of influenza in children were determined by subtypes AH1 and AH3 in similar proportions. Clinically, the predominant forms of the disease were mild (60.2%) and severe (17.9%), with no critical cases or deaths. Epidemiologically, the peak incidence of childhood influenza was reached in the period December 2023 - January 2024 for type A and March - April 2024 for type B. Compared to the previous season the number of cases of childhood influenza was reduced by about 19.6 %. The most affected age group was 1-6 years (42.4%). The clinical picture of influenza in the children studied was dominated by “classical” general signs (fever and chills, muscle ache and fatigue, headache, altered general condition) associated with respiratory manifestations (nasal obstruction, odynophagia, dysphagia, dry cough) and digestive symptoms (abdominal pain, vomiting, poor appetite). The majority of children with influenza (97.6%) were unvaccinated, and in those vaccinated the clinical forms of the disease were milder without complications. Influenza remains an important seasonal infectious disease because of its epidemiological (many cases) and socio-economic (absenteeism, additional costs) but also medical (potential for severe evolution and even death) implications.

The hidden toll of the pandemic: Excess mortality in non-COVID-19 hospital patients

Seasonal infectious diseases can cause demand and supply pressures that reduce the ability of healthcare systems to provide high-quality care. This may generate negative spillover effects on the health outcomes of patients seeking medical help for unrelated reasons. Separating these indirect burdens from the direct consequences for infected patients is usually impossible due to a lack of suitable data and an absence of population testing. However, this paper finds robust empirical evidence of excess mortality among non-COVID-19 patients in an integrated public healthcare system: the English National Health Service (NHS). Analysing the forecast error in the NHS’ model for predicted mortality, we find at least one additional excess death among patients who sought medical help for reasons unrelated to COVID-19 for every 42 COVID-19-related deaths in the population. We identify COVID-19 pressures as a key driver of non-COVID-19 excess mortality in NHS hospitals during the pandemic, and characterize the hospital populations and medical conditions that were disproportionately affected. Our findings have substantive relevance in shaping our understanding of the wider burden of COVID-19, and other seasonal diseases more generally, and can contribute to debates on optimal public health policy.

Household Influenza Transmission and Healthcare Resource Utilization Among Patients Treated with Baloxavir vs Oseltamivir: A United States Outpatient Prospective Survey.

Influenza is a common, seasonal infectious disease with broad medical, economic, and social consequences. Real-world evidence on the effect of influenza treatment on household transmission and healthcare resource utilization is limited in outpatient settings in the USA. This study examined the real-world effectiveness of baloxavir vs oseltamivir in reducing influenza household transmission and healthcare resource utilization. This prospective electronic survey on patient-reported outcomes was conducted between October 2022 and May2023 via CVS Pharmacy in the USA. Adult participants (≥ 18years old) were eligible if they filled a prescription for baloxavir or oseltamivir at a CVS Pharmacy within 2days of influenza symptom onset. Participant demographics, household transmission, and all-cause healthcare resource utilization were collected. Transmission and utilization outcomes were assessed using χ2 and Fisher exact tests. Of 87,871 unique patients contacted, 1346 (1.5%) consented. Of 374 eligible patients, 286 (90 baloxavir- and 196 oseltamivir-treated patients) completed the survey and were included in the analysis. Mean age of participants was 45.4years, 65.6% were female, and 86.7% were White. Lower household transmission was observed with baloxavir compared with oseltamivir therapy (17.8% vs 26.5%; relative risk = 0.67; 95%CI 0.41-1.11). Healthcare resource utilization, particularly emergency department visits (0.0% vs 4.6%), was also numerically lower in the baloxavir-treated group; no hospitalizations were reported in either cohort. The findings from this real-world study suggest that antiviral treatment of influenza with baloxavir may decrease household transmission and reduce healthcare resource utilization compared with oseltamivir.

Global health concern on the rising dengue and chikungunya cases in the American regions: Countermeasures and preparedness.

Severe morbidity and mortality due to seasonal infectious diseases are common global public health issues. Vector-borne viral illnesses like dengue and chikungunya overload the healthcare systems leading to critical financial burden to manage them. There is no effective drug or vaccine currently available to control these two diseases. The review was formulated by incorporating relevant reports on chikungunya and dengue in the Americas regions through a comprehensive search of literature that were available on dedicated scientific publication portals such as PubMed, ScienceDirect, and Web of Science. The strategies of public health administrations to control largely the mosquito vectors during tropical monsoon seem to be effective. Yet, it seems practically impossible to completely eliminate them. The mosquito vector disseminates the virus via transovarian route thereby internalising the virus through generations, a reason behind reappearing and recurring outbreaks. The numerous factors associated with industrialisation, urbanisation, population density, and easy transboundary movements appear to have contributed to the spread of vectors from an endemic region to elsewhere. The article made a state-of-affair comprehensive analysis of the rising dengue and chikungunya cases in the tropics, particularly the tropical Americas, as a human health concern, the countermeasures undertaken and the overall preparedness. The viral transmission is a hard situation to tackle as the vector survives in diverse temperature and ecology, is resistant to insecticides, and the unavailability of drugs. Better vector-control measures and improved understanding of the reemerging arboviral infections could offer an extended reaction time to counter outbreaks, and minimise associated morbidity/mortality.

Efficient Prediction of Seasonal Infectious Diseases Using Hybrid Machine Learning Algorithms with Feature Selection Techniques

Early seasonal disease risk identification is the most challenging task in the medical industry. The capacity to detect patients at risk of improvement throughout their hospital stay is critical for effective patient allocation and care among patients with seasonal diseases. Patient risk factor prediction is the most important issue in reducing victim mortality. Machine learning plays a vital role in identifying the risk level of patients. In this research, we consider four seasonal diseases such as dengue, malaria, typhoid, and pneumonia. In this research, the researcher finds the dangerous symptoms of victims based on their age group. The real-time dataset was used in this study. The dataset for this study was gathered from hospitals in the Madurai district between 2019 and 2020. Feature selection is the prime element of patient risk recognition. It is used to pick the most accurate attributes for prediction. The dataset is divided into 70% training data and 30% testing data. This research proposes the feature selection method Boruta-XGBoost for improving accuracy. In this research, we discuss various attribute selection algorithms, including the Boruta algorithm, the XGBoost algorithm, the recursive feature elimination method (RFE), and the PRF-BXGBoost (Patient Risk Factor-Boruta-XGBoost) algorithm. The proposed method provides greater accuracy when compared to other variable selection methods. The time complexity of the proposed method is low when compared to other algorithms.

The role of temperature in the start of seasonal infectious disease epidemics

Many infectious diseases display strong seasonal dynamics. When both hosts and parasites are influenced by seasonal variables, it is unclear if the start of an epidemic is limited by host or parasite factors or both. The Daphnia–Pasteuria host–parasite system exhibits seasonal epidemics. We aimed to ascertain how temperature contributes to the timing of P. ramosa epidemics in early spring. To this aim, we experimentally disentangled this effect from the effects of temperature on host development and phenology and from that of host traits on parasite time to visible infection. We hypothesized that the parasite is additionally directly limited by low temperatures beyond its need for available hosts. We found that parasite time to visible infection decreased with increasing temperature at a faster rate than host time to hatching and maturity did, consistent with this hypothesis. We also found that hosts hatched from sexual resting stages are less likely to become infected than those produced clonally, and that hosts resistant to many known parasite strains are slower to show signs of visible infection compared to those susceptible to many. Together, these results imply that climate change could lead to earlier seasonal epidemics for this host–parasite system, which may also impact longer‐term population dynamics.

Detrended seasonal relationships and impact of climatic factors combined with spatiotemporal effect on the prevalence of human brucellosis.

Human brucellosis (HB) is a seasonal and climate-affected infectious disease that is posing an increasing threat to public health and economy. However, most of the research on the seasonal relationships and impact of climatic factors on HB did not consider the secular trend and spatiotemporal effect related to the disease. We herein utilized long-term surveillance data on HB from 2008 to 2020 using sinusoidal models to explore detrended relationships between climatic factors and HB. In addition, we assessed the impact of such climatic factors on HB using a spatial panel data model combined with the spatiotemporal effect. HB peaked around mid-May. HB was significantly correlated with climatic factors with 1-5-month lag when the respective correlations reached the maximum across the different lag periods. Each 0.1 °C increase in temperature led to 0.5% decrease in the 5-month lag incidence of HB. We also observed a positive spatiotemporal effect on the disease. Our study provides a detailed and in-depth overview of seasonal relationships and impact of climatic factors on HB. In addition, it proposes a novel approach for exploring the seasonal relationships and quantifying the impacts of climatic factors on various infectious diseases.

Prediction of seasonal infectious diseases based on hybrid machine learning approach

Prediction of seasonal infectious diseases based on hybrid machine learning approach

AN OBSERVATIONAL STUDY TO ASSESS THE SEVERITY AND OUTCOME OF COVID 19 AND NON COVID 19 STROKE PATIENT IN TERTIARY CARE CENTER AND ITS ASSOCIATED HOSPITAL

Background: Covid-19 might be more likely to cause thrombotic vascular events, including stroke, than other coronavirus &amp; seasonal infectious diseases. The proposed mechanisms for these cerebrovascular events include a hypercoagulable state from systemic inammation &amp; cytokine storm; postinfectious immune-mediated responses. 40 COVID-19-infected patients with Stroke and 40 Non COVID-19 infected in age group 25-100 years admitted in our 500 bedded covid dedicated hospitals associated with Maharaja Yashwantrao hospital, Indore, were observed for their clinical prole, complications, laboratory parameters, inammatory markers, radiological parameters and outcome etc. Patients were divided into two study groups i.e. Covid-19 Stroke group and Non Covid-19 Stroke group. Clinical, laboratory and radiological parameters were observed in both groups and found out to be worse in covid-19 stroke group.

Relaxation of anti-COVID-19 measures reveals new challenges for infectious disease outbreak forecasting

Since early 2020, countries around the world have seen unprecedented government-imposed restrictions and behavioural changes that have changed the transmission dynamics of infectious diseases. These changes have posed unique challenges for routine seasonal infectious disease forecasting systems, which have seen the relevance of their rich long-term pre-COVID-19 datasets for prediction questioned in this new era.

Application and analysis of a model with environmental transmission in a periodic environment

&lt;abstract&gt;&lt;p&gt;The goal of this paper is to introduce a non-autonomous environmental transmission model for most respiratory and enteric infectious diseases to study the impact of periodic environmental changes on related infectious diseases. The transmission and decay rates of pathogens in the environment are set as periodic functions to summarize the influence of environmental fluctuations on diseases. The solutions of the model are qualitatively analyzed, and the equilibrium points and the reference criterion, $ R_0 $, for judging the infectivity of infectious diseases are deduced. The global stability of the disease-free equilibrium and the uniform persistence of the disease are proved by using the persistence theory. Common infectious diseases such as COVID-19, influenza, dysentery, pertussis and tuberculosis are selected to fit periodic and non-periodic models. Fitting experiments show that the periodic environmental model can respond to epidemic fluctuations more accurately than the non-periodic model. The periodic environment model is reasonable and applicable for seasonal infectious diseases. The response effects of the periodic and non-periodic models are basically the same for perennial infectious diseases. The periodic model can inform epidemiological trends in relevant emerging infectious diseases. Taking COVID-19 as an example, the sensitivity analysis results show that the virus-related parameters in the periodic model have the most significant influence on the system. It reminds us that, even late in the pandemic, we must focus on the viral load on the environment.&lt;/p&gt;&lt;/abstract&gt;

The issue of vaccine refusal: the study of a risky behavior.

Influenza is the most common seasonal infectious disease that causes permanent social, economic, and medical problems worldwide. Therefore, the most effective way to prevent influenza is through vaccines. The aim of this study is to identify the influence of factors that determine the refusal of influenza vaccine among three subjects groups. A survey was conducted amongst the three high-risk groups in 2018-2019 (Moscow, Russia). The survey involved 1,620 parents and pregnant women (group 1), 324 doctors (group 2), and 433 students (group 3). Poor vaccine uptake was observed among respondents in all three groups. According to the survey results, only 22.2% of children and 13.8% of adults were vaccinated against influenza. Group 2 showed increased rates with 36.7% of vaccinated adults and 58.7% of children. The lowest adherence to annual vaccinations was recorded in group 3 (only 17.3%). There is also a negative correlation between adherence to vaccination and smoking (-0.66), unhealthy diet (-0.73), poor oral hygiene (-0.61), and insufficient awareness of the need for influenza vaccine as well (-0.81). Thus, a general lack of vaccination awareness has a fundamental role in forming a negative attitude toward influenza vaccine. It is necessary to conduct research to promote vaccination against influenza to improve vaccine uptake among high-risk groups, particularly students.

A framework for reconstructing transmission networks in infectious diseases

In this paper, we propose a general framework for the reconstruction of the underlying cross-regional transmission network contributing to the spread of an infectious disease. We employ an autoregressive model that allows to decompose the mean number of infections into three components that describe: intra-locality infections, inter-locality infections, and infections from other sources such as travelers arriving to a country from abroad. This model is commonly used in the identification of spatiotemporal patterns in seasonal infectious diseases and thus in forecasting infection counts. However, our contribution lies in identifying the inter-locality term as a time-evolving network, and rather than using the model for forecasting, we focus on the network properties without any assumption on seasonality or recurrence of the disease. The topology of the network is then studied to get insight into the disease dynamics. Building on this, and particularly on the centrality of the nodes of the identified network, a strategy for intervention and disease control is devised.

Unpredictability in seasonal infectious diseases spread

In this work, we study the unpredictability of seasonal infectious diseases considering a SEIRS model with seasonal forcing. To investigate the dynamical behaviour, we compute bifurcation diagrams type hysteresis and their respective Lyapunov exponents. Our results from bifurcations and the largest Lyapunov exponent show bistable dynamics for all the parameters of the model. Choosing the inverse of latent period as control parameter, over 70% of the interval comprises the coexistence of periodic and chaotic attractors, bistable dynamics. Despite the competition between these attractors, the chaotic ones are preferred. The bistability occurs in two wide regions. One of these regions is limited by periodic attractors, while periodic and chaotic attractors bound the other. As the boundary of the second bistable region is composed of periodic and chaotic attractors, it is possible to interpret these critical points as tipping points. In other words, depending on the latent period, a periodic attractor (predictability) can evolve to a chaotic attractor (unpredictability). Therefore, we show that unpredictability is associated with bistable dynamics preferably chaotic, and, furthermore, there is a tipping point associated with unpredictable dynamics.

Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems

Understanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines, machine learning has advanced as the go-to technology to analyze nonlinear dynamical systems, identify patterns in big data, and make decision around them. Neural networks are now consistently used as universal function approximators for data with underlying mechanisms that are incompletely understood or exceedingly complex. However, neural networks alone ignore the fundamental laws of physics and often fail to make plausible predictions. Here we integrate data, physics, and uncertainties by combining neural networks, physics informed modeling, and Bayesian inference to improve the predictive potential of traditional neural network models. We embed the physical model of a damped harmonic oscillator into a fully-connected feed-forward neural network to explore a simple and illustrative model system, the outbreak dynamics of COVID-19. Our Physics Informed Neural Networks seamlessly integrate data and physics, robustly solve forward and inverse problems, and perform well for both interpolation and extrapolation, even for a small amount of noisy and incomplete data. At only minor additional cost, they self-adaptively learn the weighting between data and physics. They can serve as priors in a Bayesian Inference, and provide credible intervals for uncertainty quantification. Our study reveals the inherent advantages and disadvantages of Neural Networks, Bayesian Inference, and a combination of both and provides valuable guidelines for model selection. While we have only demonstrated these different approaches for the simple model problem of a seasonal endemic infectious disease, we anticipate that the underlying concepts and trends generalize to more complex disease conditions and, more broadly, to a wide variety of nonlinear dynamical systems. Our source code and examples are available at https://github.com/LivingMatterLab/xPINNs.

Environmental Factors Associated With Soil Prevalence of the Melioidosis Pathogen Burkholderia pseudomallei: A Longitudinal Seasonal Study From South West India.

Melioidosis is a seasonal infectious disease in tropical and subtropical areas caused by the soil bacterium Burkholderia pseudomallei. In many parts of the world, including South West India, most cases of human infections are reported during times of heavy rainfall, but the underlying causes of this phenomenon are not fully understood. India is among the countries with the highest predicted melioidosis burden globally, but there is very little information on the environmental distribution of B. pseudomallei and its determining factors. The present study aimed (i) to investigate the prevalence of B. pseudomallei in soil in South West India, (ii) determine geochemical factors associated with B. pseudomallei presence and (iii) look for potential seasonal patterns of B. pseudomallei soil abundance. Environmental samplings were performed in two regions during the monsoon and post-monsoon season and summer from July 2016 to November 2018. We applied direct quantitative real time PCR (qPCR) together with culture protocols to overcome the insufficient sensitivity of solely culture-based B. pseudomallei detection from soil. A total of 1,704 soil samples from 20 different agricultural sites were screened for the presence of B. pseudomallei. Direct qPCR detected B. pseudomallei in all 20 sites and in 30.2% (517/1,704) of all soil samples, whereas only two samples from two sites were culture-positive. B. pseudomallei DNA-positive samples were negatively associated with the concentration of iron, manganese and nitrogen in a binomial logistic regression model. The highest number of B. pseudomallei-positive samples (42.6%, p < 0.0001) and the highest B. pseudomallei loads in positive samples [median 4.45 × 103 genome equivalents (GE)/g, p < 0.0001] were observed during the monsoon season and eventually declined to 18.9% and a median of 1.47 × 103 GE/g in summer. In conclusion, our study from South West India shows a wide environmental distribution of B. pseudomallei, but also considerable differences in the abundance between sites and within single sites. Our results support the hypothesis that nutrient-depleted habitats promote the presence of B. pseudomallei. Most importantly, the highest B. pseudomallei abundance in soil is seen during the rainy season, when melioidosis cases occur.

Conditional power in vaccine trials with seasonal variations

ABSTRACT Conditional power (CP) is widely used in clinical trial monitoring to quantify the evidence for futility stopping or sample size adaptation during the trial. When planning an interim analysis in vaccine trials for seasonal infectious diseases, CPs calculated under the hypothesized or currently estimated effect sizes may not truly reflect future data due to seasonal variations in disease incidence and/or vaccine efficacy (VE). Relying on these estimates alone could lead to erroneous decisions. Therefore, we carried out simulation studies to investigate the use of seven different choices for the drift parameter in computing CP or predictive power (PP) in end-of-season interim analysis. Our simulations showed that, when used to inform futility stopping, CP under the hypothesized effect and a weighted PP under a normal prior distribution appear to outperform others in terms of the overall type II error rate. All CPs and PPs considered in this study resulted in comparable powers and expected sample sizes when used to inform sample size adaptation. The performance of either CP or PP largely depends on the extent to which the chosen drift parameter or the prior distribution of the drift parameter matches the remainder of the trial. Weighted CP/PP tends to be less sensitive to settings where observed data and emerging data in future seasons differ substantially as they incorporate both current estimate and future variations. Therefore, weighted strategies deserve further exploration and perhaps increased usage in guiding trial operations because they are more robust to inaccuracies in prediction. In summary, for vaccine trials with seasonal variations, a decision on trial operations should be guided by a careful consideration of plausible CPs and PPs calculated under reasonable assumptions leveraging the data, prior hypotheses, and new evidence on clinical relevance.

Correlation between mumps and meteorological factors in Xiamen City, China: A modelling study.

ObjectiveMumps is a seasonal infectious disease, always occurring in winter and spring. In this study, we aim to analyze its epidemiological characteristics, transmissibility, and its correlation with meteorological variables.MethodA seasonal Susceptible–Exposed–Infectious/Asymptomatic–Recovered model and a next-generation matrix method were applied to estimate the time-dependent reproduction number (Rt).ResultsThe seasonal double peak of annual incidence was mainly in May to July and November to December. There was high transmission at the median of Rt = 1.091 (ranged: 0 to 4.393). Rt was seasonally distributed mainly from February to April and from September to November. Correlations were found between temperature (Pearson correlation coefficient [r] ranged: from 0.101 to 0.115), average relative humidity (r = 0.070), average local pressure (r = -0.066), and the number of new cases. In addition, average local pressure (r = 0.188), average wind speed (r = 0.111), air temperature (r ranged: -0.128 to -0.150), average relative humidity (r = -0.203) and sunshine duration (r = -0.075) were all correlated with Rt.ConclusionA relatively high level of transmissibility has been found in Xiamen City, leading to a continuous epidemic of mumps. Meteorological factors, especially air temperature and relative humidity, may be more closely associated with mumps than other factors.