Forecasting Scrub Typhus Cases in Eight High-Risk Counties in China: Evaluation of Time-Series Model Performance

Scrub typhus (ST) is expanding its geographical distribution in China and in many regions worldwide raising significant public health concerns. Accurate ST time-series modeling including uncovering the role of environmental determinants is of great importance to guide disease control purposes. This study evaluated the performance of three competing time-series modeling approaches at forecasting ST cases during 2012–2020 in eight high-risk counties in China. We evaluated the performance of a seasonal autoregressive-integrated moving average (SARIMA) model, a SARIMA model with exogenous variables (SARIMAX), and the long–short term memory (LSTM) model to depict temporal variations in ST cases. In our investigation, we considered eight environmental variables known to be associated with ST landscape epidemiology, including the normalized difference vegetation index (NDVI), temperature, precipitation, atmospheric pressure, sunshine duration, relative humidity, wind speed, and multivariate El Niño/Southern Oscillation index (MEI). The first 8-year data and the last year data were used to fit the models and forecast ST cases, respectively. Our results showed that the inclusion of exogenous variables in the SARIMAX model generally outperformed the SARIMA model. Our results also indicate that the role of exogenous variables with various temporal lags varies between counties, suggesting that ST cases are temporally non-stationary. In conclusion, our study demonstrates that the approach to forecast ST cases needed to take into consideration local conditions in that time-series model performance differed between high-risk areas under investigation. Furthermore, the introduction of time-series models, especially LSTM, has enriched the ability of local public health authorities in ST high-risk areas to anticipate and respond to ST outbreaks, such as setting up an early warning system and forecasting ST precisely.

Assessment of Flood Risk Map under Climate Change RCP8.5 Scenarios in Taiwan

Climate change has exerted a significant global impact in recent years, and extreme weather-related hazards and incidents have become the new normal. For Taiwan in particular, the corresponding increase in disaster risk threatens not only the environment but also the lives, safety, and property of people. This highlights the need to develop a methodology for mapping disaster risk under climate change and delineating those regions that are potentially high-risk areas requiring adaptation to a changing climate in the future. This study provides a framework of flood risk map assessment under the RCP8.5 scenario by using different spatial scales to integrate the projection climate data of high resolution, inundation potential maps, and indicator-based approach at the end of the 21st century in Taiwan. The reference period was 1979–2003, and the future projection period was 2075–2099. High-resolution climate data developed by dynamic downscaling of the MRI-JMA-AGCM model was used to assess extreme rainfall events. The flood risk maps were constructed using two different spatial scales: the township level and the 5 km × 5 km grid. As to hazard-vulnerability(H-V) maps, users can overlay maps of their choice—such as those for land use distribution, district planning, agricultural crop distribution, or industrial distribution. Mapping flood risk under climate change can support better informed decision-making and policy-making processes in planning and preparing to intervene and control flood risks. The elderly population distribution is applied as an exposure indicator in order to guide advance preparation of evacuation plans for high-risk areas. This study found that higher risk areas are distributed mainly in northern and southern parts of Taiwan and the hazard indicators significantly increase in the northern, north-eastern, and southern regions under the RCP8.5 scenario. Moreover, the near-riparian and coastal townships of central and southern Taiwan have higher vulnerability levels. Approximately 14% of townships have a higher risk level of flooding disaster and another 3% of townships will become higher risk. For higher-risk townships, adaptation measures or strategies are suggested to prioritize improving flood preparation and protecting people and property. Such a flood risk map can be a communication tool to effectively inform decision- makers, citizens, and stakeholders about the variability of flood risk under climate change. Such maps enable decision-makers and national spatial planners to compare the relative flood risk of individual townships countrywide in order to determine and prioritize risk adaptation areas for planning spatial development policies.

Temporal and Spatial Heterogeneity of PM2.5 Related to Meteorological and Socioeconomic Factors across China during 2000–2018

In recent years, air pollution caused by PM2.5 in China has become increasingly severe. This study applied a Bayesian space–time hierarchy model to reveal the spatiotemporal heterogeneity of the PM2.5 concentrations in China. In addition, the relationship between meteorological and socioeconomic factors and their interaction with PM2.5 during 2000–2018 was investigated based on the GeoDetector model. Results suggested that the concentration of PM2.5 across China first increased and then decreased between 2000 and 2018. Geographically, the North China Plain and the Yangtze River Delta were high PM2.5 pollution areas, while Northeast and Southwest China are regarded as low-risk areas for PM2.5 pollution. Meanwhile, in Northern and Southern China, the population density was the most important socioeconomic factor affecting PM2.5 with q values of 0.62 and 0.66, respectively; the main meteorological factors affecting PM2.5 were air temperature and vapor pressure, with q values of 0.64 and 0.68, respectively. These results are conducive to our in-depth understanding of the status of PM2.5 pollution in China and provide an important reference for the future direction of PM2.5 pollution control.

Immunogenicity and waning immunity from the oral cholera vaccine (Shanchol™) in adults residing in Lukanga Swamps of Zambia

Introduction In cholera endemic areas, the periodicity of cholera outbreaks remains unpredictable, making it difficult to organize preventive efforts. Lack of data on duration of protection conferred by oral cholera vaccines further makes it difficult to determine when to deploy preemptive vaccination. We report on the immunogenicity and waning of immunity to Shanchol™ in Lukanga Swamps. Methods We enrolled a cohort of 223 participants aged between 18 and 65 years old from whom serum samples were collected at baseline, day 28 before administration of the second dose, and consecutively at 6, 12, 24, 30, 36, and 48 months. Vibriocidal antibody titres were measured and expressed as geometric mean titres. Box plots and 95% CI were computed at each visit for both Inaba and Ogawa. Seroconversion was defined as a four fold or greater increase in antibody titres compared to baseline titres. Results Overall, seroconversion against V. cholerae Inaba and Ogawa after 1st dose was 35/134 (26%) and 34/134 (25%) respectively. We observed a statistical difference in seroconversion between the two subgroups of baseline titres (low <80 and high ≥80) for both Inaba (p = 0.02) and Ogawa (p<0.0001). From a baseline of 13.58, anti-Ogawa GMT increased to 21.95 after the first dose, but rapidly waned to 14.52, 13.13, and 12.78 at months 6, 12 and 24 respectively, and then increased to 13.21, 18.67 and 23.65 at months 30, 36 and 48 respectively. A similar trend was observed for anti-Inaba GMT across the same time points. Conclusion We found that Shanchol™ was immunogenic in our study population and that vibriocidal antibodies may not be a good marker for long-term immunity. The observed rise in titres after 36 months suggests natural exposure, and this may be a critical time window opening for natural transmission in an endemic areas. We recommend re-vaccination at this time point in high risk areas.

Studi Kasus: Kejadian Bruselosis Pada Sapi Potong Di Kabupaten Barru Provinsi Sulawesi Selatan Tahun 2015-2017

There is still lack of bruselosis in beef cattle in Barru District, South Sulawesi. The aim of this study was to analyze data about the temporary distribution of disease by measuring spreading speed, and spatial distribution by mapping risk areas for bruselosis over the past three years. The data of this study was collected using the records from Dinas Peternakan and conducting interviews using structured questionnaires. This research was a descriptive study by measuring the incidence rate and describing the risk map using geographic information system (GIS). The results of this study indicate that, based on the incidence rate, the average of distribution rate of bruselosis in beef cattle in Barru is 5 cases per 10 000 heads/year. This incidence rate always decreases every year. There was no sub-district that classified as high risk. There was one area that classified as medium risk namely sub-district of Mallusetasi. Control measure that have been carried out by goverment were successful to reduce the spread of disease. Keywords: Beef cattle; Bruselosis; Incidence rate; Occurrence; Risk. Abstrak Informasi mengenai penyebaran kejadian penyakit pada sapi potong di Kabupaten Barru Sulawesi selatan masih kurang. Penelitian ini bertujuan menganalisis data distribusi kejadian penyakit secara temporal dengan mengukur kecapatan penyebaran, dan secara spasial dengan memetakan wilayah berisiko bruselosis selama tiga tahun terakhir. Data dalam penelitian ini menggunakan rekapan dari Dinas Peternakan dan wawancara mendalam menggunakan kuesioner terstruktur. Penelitian ini mengunakan metode deskriptif dengan mengukur incidence rate dan menggambarkan peta risiko menggunakan geographic information system. Hasil penelitian ini menunjukkan bahwa berdasarkan incidence rate, kecepatan rata-rata penyebaran bruselosis pada sapi potong di Kabupaten Barru sebesar 5 kasus per 10 000 ekor/tahun. Nilai incidence rate tersebut selalu menurun setiap tahunnya. Kejadian penyakit paling tinggi terjadi di Kecamatan Mallusetasi dengan incidence rate sebesar 35 kasus per 10 000 ekor/tahun. Terdapat satu wilayah yang tergolong ke dalam risiko sedang, yaitu Kecamatan Mallusetasi. Tindakan pengendalian yang telah dilakukan oleh pemerintah setempat dikatakan berhasil dalam menekan tingkat kejadian penyakit. Kata kunci: Bruselosis; Incidence rate; Risiko; Sapi potong; Tingkat kejadian

Achieving a Security Culture

A security culture can be a competitive advantage when employees uphold strong values for the protection of information and exhibit behavior that is in compliance with policies, thereby introducing minimal incidents and breaches. The security culture in an organization might, though, not be similar among departments, job levels, or even generation groups. It can pose a risk when it is not conducive to the protection of information and when security incidents and breaches occur due to employee error or negligence. This chapter aims to give organizations an overview of the concept of security culture, the factors that could influence it, an approach to assess the security culture, and to prioritize and tailor interventions for high-risk areas. The outcome of the security culture assessment can be used as input to define security awareness, training, and education programs aiding employees to exhibit behavior that is in compliance with security policies.

Spatial analysis of AIDS in the state of Maranhão: an ecological study 2011-2018

ABSTRACT Objective: Analyze the spatio-temporal distribution of AIDS cases in Maranhão. Methods: Ecological study of AIDS cases in the Notifiable Diseases Information System, 2011-2018. Gross and adjusted incidences were calculated using the Baysean method; then, the Moran Global and Local Indices to observe the existence of spatial autocorrelation of the cases and for the delimitation of high and low risk clusters. Results: 6,349 cases were reported, which were distributed heterogeneously. There was an advance of cases to new areas and persistence in old areas, such as in the capital São Luís and its surroundings. The dissemination did not occur at random, with positive spatial autocorrelation, with evidence of the formation of clusters in the municipalities of São Luís, São José de Ribamar and Paço do Lumiar. Conclusion: High-risk areas have been identified and should be considered a priority for investment in health, management, and organization of health services.