Comparison of 19 major infectious diseases during COVID-19 epidemic and previous years in Zhejiang, implications for prevention measures

Prevalence of and risk factors for infectious disease syndromic symptoms among pilgrimage to Arba'een mass gathering religious in 2023

Background

There is a growing participation of Shia Muslims, in the Arba'een pilgrimage every year. It is imperative to conduct a comprehensive study on the transmission of diseases among Arba'een pilgrims in order to identify and promptly respond to potential epidemics effectively. We aimed to assess the syndromic symptoms associated with infectious diseases and the prevalence of mask usage among patients visiting outpatient clinics during the Arba'een pilgrimage in Ira, in 2023.

Methods

In this descriptive-analytical study, 300 outpatients who sought medical care at outpatient clinics during the Arba'een pilgrimage were randomly selected. The study data and infectious disease syndromic symptoms were assessed using trained healthcare professionals. A multiple logistic regression analysis was carried out to estimate the crude and Adjusted odds ratios (AORs) of symptoms and risk factors associated with respiratory syndrome and flu-like symptoms with 95 % confidence intervals.

Results

The mean age of the participants was 39 years and 61 % of those were male. Out of 72 (24 %) of mask users, 41.6 % had changed masks in less than 8 h. The most common syndromic symptoms was Flu-like illness (53 %). In the final analysis, not wearing a mask AOR = 1.40 (1.1-9.8) and smoking AOR = 3.25 (1.1-9.5) both elevated the risk of the flu-like syndrome and severe respiratory disease symptoms.

Conclusions

Pilgrims are especially vulnerable to the flu and other respiratory illnesses. Therefore, performing a differential diagnosis in these people, including flu and COVID-19, is essential to prevent an outbreak during the Arba'een pilgrimage.

A demographic comparison and characterization of pediatric poisoning before and after the emergence of COVID-19

BACKGROUND

To compare relative rates of pediatric poisoning before and after COVID-19, including by demographic and urban-rural status, and by agent identified, using data from one university healthcare system and children's hospital.

METHODS

Using retrospective, cross sectional design from deidentified healthcare claims data, we extracted all encounters with the ICD-10-CM for Poisoning by, Adverse effects of, and Underdosing of drugs, medicants and biological substances (T36-T50) and grouped the encounters as those after state mandates regulating activity came into effect (Post-COVID-19 (3/17/2020-3/18/2021)) Pre-COVID-19 (3/18/2019-3/17/2020). We then compared poisoning agent, age at the time of the encounter, recorded sex, race, ethnicity, rural/urban residence, and visit type using Mann-Whitney U test, chi-square test of association, incidence rates and incident rate ratios between the time periods.

FINDINGS

The sample included 1608 unique patients 0-17 years of age and 4216 encounters. We also identified IRRs >1 in nearly every demographic subgroup with the exception of Non-Hispanic Blacks. The comparison of specific drugs or medicants identified a significant decrease in poisoning by Systemic antibiotics (T36); but an increase in Hormones and their synthetic substitutes and antagonists (T38), Non opioid analgesics antipyretic and antirheumatic (T39), Psychotropic Drugs (T39) and Systemic and hematologic agents (T45).

CONCLUSION

This study identifies pediatric subgroups highly affected by pediatric poisoning during the time-period immediately after the identification of COVID-19 and characterizes the drugs commonly associated with poisonings.

APPLICATION TO PRACTICE

With a further understanding nursing has the potential to impact pediatric poisoning in the inpatient, outpatient and public health setting.

The burden of infectious diseases throughout and after the COVID-19 pandemic (2020-2023) and Russo-Ukrainian war migration

Understanding how the infectious disease burden was affected throughout the COVID-19 pandemic is pivotal to identifying potential hot spots and guiding future mitigation measures. Therefore, our study aimed to analyze the changes in the rate of new cases of Poland's most frequent infectious diseases during the entire COVID-19 pandemic and after the influx of war refugees from Ukraine. We performed a registry-based population-wide study in Poland to analyze the changes in the rate of 24 infectious disease cases from 2020 to 2023 and compared them to the prepandemic period (2016-2019). Data were collected from publicly archived datasets of the Epimeld database published by national epidemiological authority institutions. The rate of most of the studied diseases (66.6%) revealed significantly negative correlations with the rate of SARS-CoV-2 infections. For the majority of infectious diseases, it substantially decreased in 2020 (in case of 83%) and 2021 (63%), following which it mostly rebounded to the prepandemic levels and, in some cases, exceeded them in 2023 when the exceptionally high annual rates of new cases of scarlet fever, Streptococcus pneumoniae infections, HIV infections, syphilis, gonococcal infections, and tick-borne encephalitis were noted. The rate of Clostridioides difficile enterocolitis was two-fold higher than before the pandemic from 2021 onward. The rate of Legionnaires' disease in 2023 also exceeded the prepandemic threshold, although this was due to a local outbreak unrelated to lifted COVID-19 pandemic restrictions or migration of war refugees. The influx of war migrants from Ukraine could impact the epidemiology of sexually transmitted diseases. The present analysis indicates that continued efforts are needed to prevent COVID-19 from overwhelming healthcare systems again and decreasing the control over the burden of other infectious diseases. It also identifies the potential tipping points that require additional mitigation measures, which are also discussed in the paper, to avoid escalation in the future.

The importance, benefits, and future of nanobiosensors for infectious diseases

Infectious diseases, caused by pathogenic microorganisms such as bacteria, viruses, parasites, or fungi, are crucial for efficient disease management, reducing morbidity and mortality rates and controlling disease spread. Traditional laboratory-based diagnostic methods face challenges such as high costs, time consumption, and a lack of trained personnel in resource-poor settings. Diagnostic biosensors have gained momentum as a potential solution, offering advantages such as low cost, high sensitivity, ease of use, and portability. Nanobiosensors are a promising tool for detecting and diagnosing infectious diseases such as coronavirus disease, human immunodeficiency virus, and hepatitis. These sensors use nanostructured carbon nanotubes, graphene, and nanoparticles to detect specific biomarkers or pathogens. They operate through mechanisms like the lateral flow test platform, where a sample containing the biomarker or pathogen is applied to a test strip. If present, the sample binds to specific recognition probes on the strip, indicating a positive result. This binding event is visualized through a colored line. This review discusses the importance, benefits, and potential of nanobiosensors in detecting infectious diseases.

Evaluating the long-term impact of COVID-19-associated public health interventions on zoonotic and vector-borne diseases in China: an interrupted time series analysis

BACKGROUND

The long-term impact of COVID-19-associated public health interventions on zoonotic and vector-borne infectious diseases (ZVBs) remains uncertain. This study sought to examine the changes in ZVBs in China during the COVID-19 pandemic and predict their future trends.

METHODS

Monthly incidents of seven ZVBs (Hemorrhagic fever with renal syndrome [HFRS], Rabies, Dengue fever [DF], Human brucellosis [HB], Leptospirosis, Malaria, and Schistosomiasis) were gathered from January 2004 to July 2023. An autoregressive fractionally integrated moving average (ARFIMA) by incorporating the COVID-19-associated public health intervention variables was developed to evaluate the long-term effectiveness of interventions and forecast ZVBs epidemics from August 2023 to December 2025.

RESULTS

Over the study period, there were 1,599,647 ZVBs incidents. HFRS and rabies exhibited declining trends, HB showed an upward trajectory, while the others remained relatively stable. The ARFIMA, incorporating a pulse pattern, estimated the average monthly number of changes of - 83 (95% confidence interval [CI] - 353-189) cases, - 3 (95% CI - 33-29) cases, - 468 (95% CI - 1531-597) cases, 2191 (95% CI 1056-3326) cases, 7 (95% CI - 24-38) cases, - 84 (95% CI - 222-55) cases, and - 214 (95% CI - 1036-608) cases for HFRS, rabies, DF, HB, leptospirosis, malaria, and schistosomiasis, respectively, although these changes were not statistically significant besides HB. ARFIMA predicted a decrease in HB cases between August 2023 and December 2025, while indicating a relative plateau for the others.

CONCLUSIONS

China's dynamic zero COVID-19 strategy may have exerted a lasting influence on HFRS, rabies, DF, malaria, and schistosomiasis, beyond immediate consequences, but not affect HB and leptospirosis. ARFIMA emerges as a potent tool for intervention analysis, providing valuable insights into the sustained effectiveness of interventions. Consequently, the application of ARFIMA contributes to informed decision-making, the design of effective interventions, and advancements across various fields.

Distribution of Infectious Disease Outbreaks in India before and after the COVID-19 Pandemic: Analysis of National Weekly Surveillance Data

ABSTRACT

National surveillance data were collected to study the outbreak trends of infectious diseases/syndromes before and during the COVID-19 pandemic period, spanning from 2018 to 2022. The study found that out of 4208 outbreaks, 2972 occurred before the COVID-19 period, affecting 147,425 people, while 1236 outbreaks affected 52,324 people during the pandemic. The number of outbreaks for diseases such as acute flaccid paralysis, fever with rashes, leptospirosis, rabies, and scrub typhus increased during the pandemic. The geographic distribution of outbreaks remained similar for some reemerging diseases in both periods. The trends for dengue, Japanese encephalitis, and cholera remained consistent with peaks mostly from July to September in both periods. We observed a considerable reduction in morbidity and mortality due to outbreaks in India during the pandemic. Despite similar distributional patterns, the study indicates a strong suspicion of persistent outbreak-initiating factors, necessitating an efficient and vigilant surveillance system in the country.

Global-, Regional-, and National-Level Impacts of the COVID-19 Pandemic on Tuberculosis Diagnoses, 2020-2021

Evaluating cross-country variability on the impact of the COVID-19 pandemic on tuberculosis (TB) may provide urgent inputs to control programs as countries recover from the pandemic. We compared expected TB notifications, modeled using trends in annual TB notifications from 2013-2019, with observed TB notifications to compute the observed to expected (OE) ratios for 170 countries. We applied the least absolute shrinkage and selection operator (LASSO) method to identify the covariates, out of 27 pandemic- and tuberculosis-relevant variables, that had the strongest explanatory power for log OE ratios. The COVID-19 pandemic was associated with a 1.55 million (95% CI: 1.26-1.85, 21.0% [17.5-24.6%]) decrease in TB diagnoses in 2020 and a 1.28 million (0.90-1.76, 16.6% [12.1-21.2%]) decrease in 2021 at a global level. India, Indonesia, the Philippines, and China contributed the most to the global declines for both years, while sub-Saharan Africa achieved pre-pandemic levels by 2021 (OE ratio = 1.02 [0.99-1.05]). Age-stratified analyses revealed that the ≥ 65-year-old age group experienced greater relative declines in TB diagnoses compared with the under 65-year-old age group in 2020 (RR = 0.88 [0.81-0.96]) and 2021 (RR = 0.88 [0.79-0.98]) globally. Covariates found to be associated with all-age OE ratios in 2020 were age-standardized smoking prevalence in 2019 (β = 0.973 [0.957-990]), school closures (β = 0.988 [0.977-0.998]), stay-at-home orders (β = 0.993 [0.985-1.00]), SARS-CoV-2 infection rate (β = 0.991 [0.987-0.996]), and proportion of population ≥65 years (β = 0.971 [0.944-0.999]). Further research is needed to clarify the extent to which the observed declines in TB diagnoses were attributable to disruptions in health services, decreases in TB transmission, and COVID-19 mortality among TB patients.

The effects of the COVID-19 pandemic on dengue cases in Malaysia

Background

Globally, the COVID-19 pandemic has affected the transmission dynamics and distribution of dengue. Therefore, this study aims to describe the impact of the COVID-19 pandemic on the geographic and demographic distribution of dengue incidence in Malaysia.

Methods

This study analyzed dengue cases from January 2014 to December 2021 and COVID-19 confirmed cases from January 2020 to December 2021 which was divided into the pre (2014 to 2019) and during COVID-19 pandemic (2020 to 2021) phases. The average annual dengue case incidence for geographical and demographic subgroups were calculated and compared between the pre and during the COVID-19 pandemic phases. In addition, Spearman rank correlation was performed to determine the correlation between weekly dengue and COVID-19 cases during the COVID-19 pandemic phase.

Results

Dengue trends in Malaysia showed a 4-year cyclical trend with dengue case incidence peaking in 2015 and 2019 and subsequently decreasing in the following years. Reductions of 44.0% in average dengue cases during the COVID-19 pandemic compared to the pre-pandemic phase was observed at the national level. Higher dengue cases were reported among males, individuals aged 20-34 years, and Malaysians across both phases. Weekly dengue cases were significantly correlated (ρ = -0.901) with COVID-19 cases during the COVID-19 pandemic.

Conclusion

There was a reduction in dengue incidence during the COVID-19 pandemic compared to the pre-pandemic phase. Significant reductions were observed across all demographic groups except for the older population (>75 years) across the two phases.

The short and long-term impact of nonpharmaceutical interventions on the prevalence of varicella in Xi'an during the COVID-19 pandemic

Varicella is a highly prevalent infectious disease with a similar transmission pathway to coronavirus disease 2019 (COVID-19). In the context of the COVID-19 pandemic, anti-COVID-19 nonpharmaceutical interventions (NPIs) have been implemented to prevent the spread of the infection. This study aims to analyze varicella's epidemiological characteristics and further investigate the effect of anti-COVID-19 NPIs on varicella in Xi'an, northwestern China. Based on the varicella surveillance data, search engine indices, meteorological factors from 2011 to 2021 in Xi'an, and different levels of emergency response to COVID-19 during the pandemic, we applied Bayesian Structural Time Series models and interrupted time series analysis to predict the counterfactual incidence of varicella and quantify the impact of varying NPIs intensities on varicella. From 2011 to 2021, varicella incidence increased, especially in 2019, with a high incidence of 111.69/100 000. However, there was a sharp decrease of 43.18% in 2020 compared with 2019, and the peak of varicella incidence in 2020 was lower than in previous years from the 21st to the 25th week. In 2021, the seasonality of varicella incidence gradually returned to a seasonal pattern in 2011-2019. The results suggest that anti-COVID-19 NPIs effectively reduce the incidence of varicella, and the reduction has spatiotemporal heterogeneity.

Impact of implementing a free varicella vaccination policy on incidence in Wuxi City, China: an interrupted time series analysis

Varicella vaccination is optional and requires self-payment. On 1 December 2018, Wuxi City launched a free varicella vaccination program for children. This study aimed to evaluate the changes in varicella incidence before and after the implementation of the policy. The data were obtained from official information systems and statistical yearbooks. We divided the period into chargeable (January 2017 to November 2018) and free (December 2018 to December 2021) periods. Interrupt time series analysis was used to conduct a generalised least-squares regression analysis for the two periods. A total of 51,071 varicella cases were reported between January 2017 and December 2021. After the implementation of the policy, there was a statistically significant decrease in the incidence of varicella (β2 = -0.140, P = 0.017), and the slope of the incidence also decreased by 0.012 (P = 0.015). Following policy implementation, the incidence decreased in all age groups, with the largest decline observed among children aged 8-14 years (β2 = -1.109, P = 0.009), followed by children aged ≤7 years (β2 = -0.894, P = 0.013). Our study found a significant reduction in the incidence of varicella in the total population after the introduction of free varicella vaccination in Wuxi City.

The indirect impacts of nonpharmacological COVID-19 control measures on other infectious diseases in Yinchuan, Northwest China: a time series study

BACKGROUND

Various nonpharmaceutical interventions (NPIs) against COVID-19 continue to have an impact on socioeconomic and population behaviour patterns. However, the effect of NPIs on notifiable infectious diseases remains inconclusive due to the variability of the disease spectrum, high-incidence endemic diseases and environmental factors across different geographical regions. Thus, it is of public health interest to explore the influence of NPIs on notifiable infectious diseases in Yinchuan, Northwest China.

METHODS

Based on data on notifiable infectious diseases (NIDs), air pollutants, meteorological data, and the number of health institutional personnel in Yinchuan, we first fitted dynamic regression time series models to the incidence of NIDs from 2013 to 2019 and then estimated the incidence for 2020. Then, we compared the projected time series data with the observed incidence of NIDs in 2020. We calculated the relative reduction in NIDs at different emergency response levels in 2020 to identify the impacts of NIPs on NIDs in Yinchuan.

RESULTS

A total of 15,711 cases of NIDs were reported in Yinchuan in 2020, which was 42.59% lower than the average annual number of cases from 2013 to 2019. Natural focal diseases and vector-borne infectious diseases showed an increasing trend, as the observed incidence in 2020 was 46.86% higher than the estimated cases. The observed number of cases changed in respiratory infectious diseases, intestinal infectious diseases and sexually transmitted or bloodborne diseases were 65.27%, 58.45% and 35.01% higher than the expected number, respectively. The NIDs with the highest reductions in each subgroup were hand, foot, and mouth disease (5854 cases), infectious diarrhoea (2157 cases) and scarlet fever (832 cases), respectively. In addition, it was also found that the expected relative reduction in NIDs in 2020 showed a decline across different emergency response levels, as the relative reduction dropped from 65.65% (95% CI: -65.86%, 80.84%) during the level 1 response to 52.72% (95% CI: 20.84%, 66.30%) during the level 3 response.

CONCLUSIONS

The widespread implementation of NPIs in 2020 may have had significant inhibitory effects on the incidence of respiratory infectious diseases, intestinal infectious diseases and sexually transmitted or bloodborne diseases. The relative reduction in NIDs during different emergency response levels in 2020 showed a declining trend as the response level changed from level 1 to level 3. These results can serve as essential guidance for policy-makers and stakeholders to take specific actions to control infectious diseases and protect vulnerable populations in the future.

Machine learning clinical decision support systems for surveillance: a case study on pertussis and RSV in children

We tested the performance of a machine learning (ML) algorithm based on signs and symptoms for the diagnosis of RSV infection or pertussis in the first year of age to support clinical decisions and provide timely data for public health surveillance. We used data from a retrospective case series of children in the first year of life investigated for acute respiratory infections in the emergency room from 2015 to 2020. We collected data from PCR laboratory tests for confirming pertussis or RSV infection, clinical symptoms, and routine blood testing results, which were used for the algorithm development. We used a LightGBM model to develop 2 sets of models for predicting pertussis and RSV infection: for each type of infection, we developed one model trained with the combination of clinical symptoms and results from routine blood test (white blood cell count, lymphocyte fraction and C-reactive protein), and one with symptoms only. All analyses were performed using Python 3.7.4 with Shapley values (Shap values) visualization package for predictor visualization. The performance of the models was assessed through confusion matrices. The models were developed on a dataset of 599 children. The recall for the pertussis model combining symptoms and routine laboratory tests was 0.72, and 0.74 with clinical symptoms only. For RSV infection, recall was 0.68 with clinical symptoms and laboratory tests and 0.71 with clinical symptoms only. The F1 score for the pertussis model was 0.72 in both models, and, for RSV infection, it was 0.69 and 0.75. ML models can support the diagnosis and surveillance of infectious diseases such as pertussis or RSV infection in children based on common symptoms and laboratory tests. ML-based clinical decision support systems may be developed in the future in large networks to create accurate tools for clinical support and public health surveillance.

Hygiene Behavior and COVID-19 Pandemic: Opportunities of COVID-19-Imposed Changes in Hygiene Behavior

In Ethiopia, the WHO strategies to stop coronavirus transmission were implemented rapidly. As a result, there was a rapid change in hygiene behavior, which are basic for preventing COVID-19 and other contagious diseases. This research was designed to examine the sustainability of the COVID-19 imposed hygiene behaviors for future challenges. The study was conducted in 2 major nexus areas in Addis Ababa. The data were collected using a questionnaire and spot-check from 622 respondents selected by systematic random sampling. The questionnaire was given at every 15th interval in several spots of the site. Observational hygiene-check was done through observing key personal hygiene conditions. Proportion, χ2 test, and Poisson's regression were applied for the analysis. The χ2-test analyses showed that the hand washing frequency before, during, and post-COVID-19 was statistically significant (P < .005). Findings from the spot-check also show that the hands of 76.8%, the nails of 68.7%, and the hairs of 70.7% of the respondents were clean. The major driving factors for the rapid changes in hygiene behavior were the awareness developed (95%), the fear and panic (90%), and increased access to water and soap (63%). Nevertheless, the major reasons for failing to continue the COVID-19-imposed good hygiene practice in the post-COVID-19 times include the decline in infection and death rates (26%) and the decline in facility access (20%). Hand washing frequency significantly changed during the COVID-19 pandemic indicating that the practice as part of the preventive strategy was successful. However, as this was mainly due to the fear and panic in the community, the COVID-19 imposed hand washing practice did not bring real and sustainable behavioral changes. This indicates that for long-lasting changes in hygiene behavior, continuous and better approach need to be introduced.

Coinfections with SARS-CoV-2 variants and influenza virus during the 2019 Coronavirus disease pandemic in Burkina Faso: A surveillance study

Background and Aim

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) particularly the variants of concern coinfections with influenza is a public health concern in Africa. We aimed to characterize the SARS-CoV-2 variants and determine the rate of coinfections with influenza in Burkina Faso.

Methods

COVID-19 surveillance study was conducted between August 2021 and January 2022 using reverse transcription polymerase chain reaction (RT-PCR). Positive specimens were further screened for SARS-CoV-2 variants using the multiple variants real-time PCR kits. In addition, influenza virus strains were detected by RT-PCR in SARS-CoV-2 positive specimens using the CDC primers, probes, and protocols.

Results

Of 324 specimens assessed, the Omicron and Delta variants of SARS-CoV-2 were the most prevalent with 27.2% [95% confident interval (CI): 22.5-32.4] and 22.2% [95% CI: 17.9-27.2], respectively. The Beta and Gamma variants were detected in 4.3% [95% CI: 2.4-7.1] and 0.3% [95% CI: 0.0-1.7], respectively. Coinfections of Omicron and Beta variants were reported in 21.3% [95% CI: 17.0-26.2], Omicron and Delta variants in 1.2% [95% CI: 0.3-3.1] of specimens, and the Omicron-Gamma variants' coinfections in 0.6% [95% CI: 0.1-2.2]. One COVID-19 specimen with an undetected SARS-CoV-2 variant was also tested positive for the seasonal influenza A (H3N2) virus. No cases of pandemic influenza A (H1N1)pdm09, seasonal A/H1N1, and influenza B were detected.

Conclusions

The current World Health Organization SARS-CoV-2 variants of concern were prevalent and their coinfections with influenza were uncommon. Continuous surveillance of both pathogens is, however, needed because of their public health implications.

Spatiotemporally comparative analysis of three common infectious diseases in China during 2013-2015

BACKGROUND

Dengue fever (DF), influenza, and hand, foot, and mouth disease (HFMD) have had several various degrees of outbreaks in China since the 1900s, posing a serious threat to public health. Previous studies have found that these infectious diseases were often prevalent in the same areas and during the same periods in China.

METHODS

This study combined traditional descriptive statistics and spatial scan statistic methods to analyze the spatiotemporal features of the epidemics of DF, influenza, and HFMD during 2013-2015 in mainland China at the provincial level.

RESULTS

DF got an intensive outbreak in 2014, while influenza and HFMD were stable from 2013 to 2015. DF mostly occurred during August-November, influenza appeared during November-next March, and HFMD happened during April-November. The peaks of these diseases form a year-round sequence; Spatially, HFMD generally has a much higher incidence than influenza and DF and covers larger high-risk areas. The hotspots of influenza tend to move from North China to the southeast coast. The southeastern coastal regions are the high-incidence areas and the most significant hotspots of all three diseases.

CONCLUSIONS

This study suggested that the three diseases can form a year-round sequence in southern China, and the southeast coast of China is a particularly high-risk area for these diseases. These findings may have important implications for the local public health agency to allocate the prevention and control resources.

Roles for Pathogen Interference in Influenza Vaccination, with Implications to Vaccine Effectiveness (VE) and Attribution of Influenza Deaths

Pathogen interference is the ability of one pathogen to alter the course and clinical outcomes of infection by another. With up to 3000 species of human pathogens the potential combinations are vast. These combinations operate within further immune complexity induced by infection with multiple persistent pathogens, and by the role which the human microbiome plays in maintaining health, immune function, and resistance to infection. All the above are further complicated by malnutrition in children and the elderly. Influenza vaccination offers a measure of protection for elderly individuals subsequently infected with influenza. However, all vaccines induce both specific and non-specific effects. The specific effects involve stimulation of humoral and cellular immunity, while the nonspecific effects are far more nuanced including changes in gene expression patterns and production of small RNAs which contribute to pathogen interference. Little is known about the outcomes of vaccinated elderly not subsequently infected with influenza but infected with multiple other non-influenza winter pathogens. In this review we propose that in certain years the specific antigen mix in the seasonal influenza vaccine inadvertently increases the risk of infection from other non-influenza pathogens. The possibility that vaccination could upset the pathogen balance, and that the timing of vaccination relative to the pathogen balance was critical to success, was proposed in 2010 but was seemingly ignored. Persons vaccinated early in the winter are more likely to experience higher pathogen interference. Implications to the estimation of vaccine effectiveness and influenza deaths are discussed.

A global one health perspective on leptospirosis in humans and animals

Leptospirosis is a quintessential one health disease of humans and animals caused by pathogenic spirochetes of the genus Leptospira. Intra- and interspecies transmission is dependent on 1) reservoir host animals in which organisms replicate and are shed in urine over long periods of time, 2) the persistence of spirochetes in the environment, and 3) subsequent human-animal-environmental interactions. The combination of increased flooding events due to climate change, changes in human-animal-environmental interactions as a result of the pandemic that favor a rise in the incidence of leptospirosis, and under-recognition of leptospirosis because of nonspecific clinical signs and severe signs that resemble COVID-19 represents a "perfect storm" for resurgence of leptospirosis in people and domestic animals. Although often considered a disease that occurs in warm, humid climates with high annual rainfall, pathogenic Leptospira spp have recently been associated with disease in animals and humans that reside in semiarid regions like the southwestern US and have impacted humans that have a wide spectrum of socioeconomic backgrounds. Therefore, it is critical that physicians, veterinarians, and public health experts maintain a high index of suspicion for the disease regardless of geographic and socioeconomic circumstances and work together to understand outbreaks and implement appropriate control measures. Over the last decade, major strides have been made in our understanding of the disease because of improvements in diagnostic tests, molecular epidemiologic tools, educational efforts on preventive measures, and vaccines. These novel approaches are highlighted in the companion Currents in One Health by Sykes et al, AJVR, September 2022.

Spatio-temporal evolution and trend prediction of the incidence of Class B notifiable infectious diseases in China: a sample of statistical data from 2007 to 2020

BACKGROUND

With the accelerated global integration and the impact of climatic, ecological and social environmental changes, China will continue to face the challenge of the outbreak and spread of emerging infectious diseases and traditional ones. This study aims to explore the spatial and temporal evolutionary characteristics of the incidence of Class B notifiable infectious diseases in China from 2007 to 2020, and to forecast the trend of it as well. Hopefully, it will provide a reference for the formulation of infectious disease prevention and control strategies.

METHODS

Data on the incidence rates of Class B notifiable infectious diseases in 31 provinces, municipalities and autonomous regions of China from 2007 to 2020 were collected for the prediction of the spatio-temporal evolution and spatial correlation as well as the incidence of Class B notifiable infectious diseases in China based on global spatial autocorrelation and Autoregressive Integrated Moving Average (ARIMA).

RESULTS

From 2007 to 2020, the national incidence rate of Class B notifiable infectious diseases (from 272.37 per 100,000 in 2007 to 190.35 per 100,000 in 2020) decreases year by year, and the spatial distribution shows an "east-central-west" stepwise increase. From 2007 to 2020, the spatial clustering of the incidence of Class B notifiable infectious diseases is significant and increasing year by year (Moran's I index values range from 0.189 to 0.332, p < 0.05). The forecasted incidence rates of Class B notifiable infectious diseases nationwide from 2021 to 2024 (205.26/100,000, 199.95/100,000, 194.74/100,000 and 189.62/100,000) as well as the forecasted values for most regions show a downward trend, with only some regions (Guangdong, Hunan, Hainan, Tibet, Guangxi and Guizhou) showing an increasing trend year by year.

CONCLUSIONS

The current study found that since there were significant regional disparities in the prevention and control of infectious diseases in China between 2007 and 2020, the reduction of the incidence of Class B notifiable infectious diseases requires the joint efforts of the surrounding provinces. Besides, special attention should be paid to provinces with an increasing trend in the incidence of Class B notifiable infectious diseases to prevent the re-emergence of certain traditional infectious diseases in a particular province or even the whole country, as well as the outbreak and spread of emerging infectious diseases.