Estimating the spatial risk of tuberculosis distribution in Gurage zone, southern Ethiopia: a geostatistical kriging approach

Japanese encephalitis transmission trends in Gansu, China: A time series predictive model based on spatial dispersion

Objective

This study serves to ascertain trends of space and time for Japanese encephalitis (JE) transmission at the township-level and develop an innovative time series predictive model to predict the geographical spread of JE in Gansu Province, China.

Methods

We collected weekly data on JE from 2005 to 2019 at the township-level. Kriging interpolation maps were used to visualize the trend of the epidemic spread of JE, and linear regression models were used to calculate the monthly changes in minimum longitude and maximum latitude of emerging towns with JE to assess the speed of the epidemic's spread to the northwest. Additionally, we utilized a time series Seasonal Autoregressive Integrated Moving Average (SARIMA) model to dynamically predict the ongoing weekly number of JE emerging townships.

Results

The Kriging difference map revealed a significant trend of JE spread towards the northwest. Our regression model indicated that the rate of decrease in minimum longitude was approximately 0.64 km per month, while the rate of increase in maximum latitude was approximately 1.00 km per month. Furthermore, the SARIMA pattern (2,0,0)(2,0,1)₅₂ exhibited a better goodness-of-fit for predicting JE transmission, with an overall agreement of 93.27% to 94.23%.

Conclusion

Our study highlights the expansion of JE cases towards the northwest of Gansu, indicating the need for ongoing surveillance and control efforts. The use of the SARIMA model provides a valuable tool for predicting the trend of JE spatial dispersion, thereby improving early warning systems. Our findings suggest that the number of emerging townships can be used to predict the trend of JE spatial dispersion, providing crucial insights for future research on JE incidence.

Metropolitan age-specific mortality trends at borough and neighborhood level: The case of Mexico City

Understanding the spatial and temporal patterns of mortality rates in a highly heterogeneous metropolis, is a matter of public policy interest. In this context, there is no, to the best of our knowledge, previous studies that correlate both spatio-temporal and age-specific mortality rates in Mexico City. Spatio-temporal Kriging modeling was used over five age-specific mortality rates (from the years 2000 to 2016 in Mexico City), to gain both spatial (borough and neighborhood) and temporal (year and trimester) data level description. Mortality age-specific patterns have been modeled using multilevel modeling for longitudinal data. Posterior tests were carried out to compare mortality averages between geo-spatial locations. Mortality correlation extends in all study groups for as long as 12 years and as far as 13.27 km. The highest mortality rate takes place in the Cuauhtémoc borough, the commercial, touristic and cultural core downtown of Mexico City. On the contrary, Tlalpan borough is the one with the lowest mortality rates in all the study groups. Post-productive mortality is the first age-specific cause of death, followed by infant, productive, pre-school and scholar groups. The combinations of spatio-temporal Kriging estimation and time-evolution linear mixed-effect models, allowed us to unveil relevant time and location trends that may be useful for public policy planning in Mexico City.