METHODS: We assembled 1155 geographical records of yellow fever virus infection in people from 1970 to 2016. We used a Poisson point process boosted regression tree model that explicitly incorporated environmental and biological explanatory covariates, vaccination coverage, and spatial variability in disease reporting rates to predict the relative risk of apparent yellow fever virus infection at a 5 × 5 km resolution across all risk zones (47 countries across the Americas and Africa). We also used the fitted model to predict the receptivity of areas outside at-risk zones to the introduction or reintroduction of yellow fever transmission. By use of previously published estimates of annual national case numbers, we used the model to map subnational variation in incidence of yellow fever across at-risk countries and to estimate the number of cases averted by vaccination worldwide.
FINDINGS: Substantial international and subnational spatial variation exists in relative risk and incidence of yellow fever as well as varied success of vaccination in reducing incidence in several high-risk regions, including Brazil, Cameroon, and Togo. Areas with the highest predicted average annual case numbers include large parts of Nigeria, the Democratic Republic of the Congo, and South Sudan, where vaccination coverage in 2016 was estimated to be substantially less than the recommended threshold to prevent outbreaks. Overall, we estimated that vaccination coverage levels achieved by 2016 avert between 94 336 and 118 500 cases of yellow fever annually within risk zones, on the basis of conservative and optimistic vaccination scenarios. The areas outside at-risk regions with predicted high receptivity to yellow fever transmission (eg, parts of Malaysia, Indonesia, and Thailand) were less extensive than the distribution of the main urban vector, A aegypti, with low receptivity to yellow fever transmission in southern China, where A aegypti is known to occur.
INTERPRETATION: Our results provide the evidence base for targeting vaccination campaigns within risk zones, as well as emphasising their high effectiveness. Our study highlights areas where public health authorities should be most vigilant for potential spread or importation events.
FUNDING: Bill & Melinda Gates Foundation.
METHODS: Data on dengue infection were extracted from the dengue database of the state of Sabah from 2013 through 2018. DENV NS-1-positive serum samples from multiple sites throughout Sabah were sent to the state public health laboratory, Kota Kinabalu Public Health Laboratory, for serotype determination. The analysis of factors associated with severe dengue was determined from the data of 2018 only.
RESULTS: In 2013, there were 724 dengue cases; however, from 2014, dengue cases increased exponentially and resulted in 3423 cases in 2018. Increasing dengue cases also led to increased dengue mortality. The number of dengue deaths in 2013 was only five which then gradually increased, and in 2018, 29 patients died. This is an increase of 580% from 2013 to 2018. Deaths were considerably more in the districts of the east coast of Sabah compared with districts in the west coast. During the study period, all DENV serotypes could be identified as serotypes circulating in Sabah. In 2018, the predominant serotype was DENV-3. The monthly peak of dengue infection varied in different years. In the logistic regression analysis, it was identified that children were 6.5 times, patients infected with mixed serotype of DENV were 13 times, and cases from the districts of the east coast were 5.2 times more likely to develop severe dengue.
CONCLUSIONS: An increasing trend of dengue infection has been observed in Sabah. The burden of dengue, severe dengue, and mortality was noted especially in the districts of the east coast of Sabah. Severe dengue was most likely developed in children, cases from the east coast, and patients infected with mixed serotype of DENV.
METHODS: A total of three databases were searched on September 15, 2020: PubMed, Web of Science, and Science Direct. The searches were conducted using a pre-specified search strategy to record studies reported the reproductive number of coronavirus from its inception in December 2019. It includes keywords of coronavirus and its reproductive number, which were combined using the Boolean operators (AND, OR). Based on the included studies, we estimated a summary reproductive number by using the meta-analysis. We used narrative synthesis to explain the results of the studies where the reproductive number was reported, however, were not possible to include in the meta-analysis because of the lack of data (mostly due to confidence interval was not reported).
RESULTS: Total of 42 studies included in this review whereas 29 of them were included in the meta-analysis. The estimated summary reproductive number was 2.87 (95% CI, 2.39-3.44). We found evidence of very high heterogeneity (99.5%) of the reproductive number reported in the included studies. Our sub-group analysis was found the significant variations of reproductive number across the country for which it was estimated, method and model that were used to estimate the reproductive number, number of case that was considered to estimate the reproductive number, and the type of reproductive number that was estimated. The highest reproductive number was reported for the Diamond Princess Cruise Ship in Japan (14.8). In the country-level, the higher reproductive number was reported for France (R, 6.32, 95% CI, 5.72-6.99) following Germany (R, 6.07, 95% CI, 5.51-6.69) and Spain (R, 3.56, 95% CI, 1.62-7.82). The higher reproductive number was reported if it was estimated by using the Markov Chain Monte Carlo method (MCMC) method and the Epidemic curve model. We also reported significant heterogeneity of the type of reproductive number- a high-value reported if it was the time-dependent reproductive number.
CONCLUSION: The estimated summary reproductive number indicates an exponential increase of coronavirus infection in the coming days. Comprehensive policies and programs are important to reduce new infections as well as the associated adverse consequences including death.