Assessment of the Current Surveillance System for Human Leptospirosis in Ecuador by Decision Analytic Modeling

Prevalence and risk factors of bovine leptospirosis in the Ecuadorian Amazon

Background and Aim

Leptospirosis is an infectious zoonotic disease that significantly affects animal health, particularly the reproduction of ruminants. However, some aspects of epidemiology and clinical characteristics have not been clarified. This study aimed to estimate the prevalence and identify risk factors of leptospirosis in female bovines at reproductive age in the Ecuadorian Amazon rainforest.

Materials and Methods

A total of 213 bovines were studied in the Amazon province of Zamora Chinchipe, in which a microscopic agglutination test was used to diagnose a panel of eight serovars of Leptospira borgpetersenii (Sejroe) and Leptospira interrogans, Australis, Bataviae, Canicola, Tarassovi, Icterohaemorrhagiae, Wolffi, and Hardjo. An epidemiological survey was conducted to identify risk factors by animal and herd and clinical symptoms associated with Leptospira spp. infection; and blood samples were collected to determine the differences between seropositive and seronegative animals regarding hematocrit, hemoglobin (Hb), mean corpuscular Hb concentration, total red blood cell count, total platelet count, leukocytes, total proteins, creatinine, and ureic nitrogen.

Results

The prevalence of bovine leptospirosis was 12.21% (26/213), with positive reactions in the Australis, Sejroe, Bataviae, Canicola, and Tarassovi serovars. No variables were considered risk factors, nor clinical signs associated with the infection, nor were there differences in the hematological parameters between the seropositive and seronegative animals.

Conclusion

These findings indicate the persistence of Leptospira on cattle farms in the Ecuadorian Amazon and highlight the interaction between domestic and wild species. It is crucial to implement control measures and improvements in management practices under the One Health approach to reduce accidental infections from contact with wildlife; the awareness of farmers is essential for effective prevention.

Utility of surveillance data for planning for dengue elimination in Yogyakarta, Indonesia: a scenario-tree modelling approach

INTRODUCTION

Field trials and modelling studies suggest that elimination of dengue transmission may be possible through widespread release of Aedes aegypti mosquitoes infected with the insect bacterium Wolbachia pipientis (wMel strain), in conjunction with routine dengue control activities. This study aimed to develop a modelling framework to guide planning for the potential elimination of locally acquired dengue in Yogyakarta, a city of almost 400 000 people in Java, Indonesia.

METHODS

A scenario-tree modelling approach was used to estimate the sensitivity of the dengue surveillance system (including routine hospital-based reporting and primary-care-based enhanced surveillance), and time required to demonstrate elimination of locally acquired dengue in Yogyakarta city, assuming the detected incidence of dengue decreases to zero in the future. Age and gender were included as risk factors for dengue, and detection nodes included the probability of seeking care, probability of sample collection and testing, diagnostic test sensitivity and probability of case notification. Parameter distributions were derived from health system data or estimated by expert opinion. Alternative simulations were defined based on changes to key parameter values, separately and in combination.

RESULTS

For the default simulation, median surveillance system sensitivity was 0.131 (95% PI 0.111 to 0.152) per month. Median confidence in dengue elimination reached 80% after a minimum of 13 months of zero detected dengue cases and 90% confidence after 25 months, across different scenarios. The alternative simulations investigated produced relatively small changes in median system sensitivity and time to elimination.

CONCLUSION

This study suggests that with a combination of hospital-based surveillance and enhanced clinic-based surveillance for dengue, an acceptable level of confidence (80% probability) in the elimination of locally acquired dengue can be reached within 2 years. Increasing the surveillance system sensitivity could shorten the time to first ascertainment of elimination of dengue and increase the level of confidence in elimination.

Enhancing Insights into Australia's Gonococcal Surveillance Programme through Stochastic Modelling

Continued surveillance of antimicrobial resistance is critical as a feedback mechanism for the generation of concerted public health action. A characteristic of importance in evaluating disease surveillance systems is representativeness. Scenario tree modelling offers an approach to quantify system representativeness. This paper utilises the modelling approach to assess the Australian Gonococcal Surveillance Programme's representativeness as a case study. The model was built by identifying the sequence of events necessary for surveillance output generation through expert consultation and literature review. A scenario tree model was developed encompassing 16 dichotomous branches representing individual system sub-components. Key classifications included biological sex, clinical symptom status, and location of healthcare service access. The expected sensitivities for gonococcal detection and antibiotic status ascertainment were 0.624 (95% CI; 0.524, 0.736) and 0.144 (95% CI; 0.106, 0.189), respectively. Detection capacity of the system was observed to be high overall. The stochastic modelling approach has highlighted the need to consider differential risk factors such as sex, health-seeking behaviours, and clinical behaviour in sample generation. Actionable points generated by this study include modification of clinician behaviour and supplementary systems to achieve a greater contextual understanding of the surveillance data generation process.