Community resilience demonstrated through a Te Ao Māori (Ngāti Manawa) lens: The Rāhui

INTRODUCTION: This research project is associated with a small rural community utilising the Te Ao Māori (Ngāti Manawa) understanding of Rāhui, as a means of decreasing the possibility of negative impacts for their mostly Māori population, during the Covid-19 pandemic that was experienced in March 2020 in Aotearoa New Zealand. Rāhui is a conservation measure shrouded in tapu designed to limit, restrict or prevent access to the natural environment. For example, Te Wao Tapu nui a Tāne protecting in the process the mauri of our rivers, lakes, streams following a mishap or misfortune such as a drowning. Equally as important, Rāhui was used as a proactive means of conservation.METHOD: Using mixed methods, this study highlights both positive and challenging experiences in the statistical and thematic analysis that may inform future public health planning for the inevitable and ongoing effects of pandemic responses in Aotearoa New Zealand which are potentially transportable beyond Aotearoa New Zealand.IMPLICATIONS: This research identified how Nga ̄ti Manawa of Murupara, utilised Rāhui as a mechanism of resilience in order to keep local residents thriving and healthy during and after the Covid-19 pandemic lockdown by setting up checkpoints on the borders of their rohe, and restricting the vehicle and human traffic into Murupara. Support for the Rāhui was significant from five hapū leaders and from the community survey illuminating a sense of safety that the checkpoints offered to a vulnerable and mostly Māori rural community.

Integrating Spatial Modelling and Space–Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan

The spatial–temporal assessment of vector diseases is imperative to design effective action plans and establish preventive strategies. Therefore, such assessments have potential public health planning-related implications. In this context, we here propose an integrated spatial disease evaluation (I-SpaDE) framework. The I-SpaDE integrates various techniques such as the Kernel Density Estimation, the Optimized Hot Spot Analysis, space–time assessment and prediction, and the Geographically Weighted Regression (GWR). It makes it possible to systematically assess the disease concentrations, patterns/trends, clustering, prediction dynamics, and spatially varying relationships between disease and different associated factors. To demonstrate the applicability and effectiveness of the I-SpaDE, we apply it in the second largest city of Pakistan, namely Lahore, using Dengue Fever (DF) during 2007–2016 as an example vector disease. The most significant clustering is evident during the years 2007–2008, 2010–2011, 2013, and 2016. Mostly, the clusters are found within the city’s central functional area. The prediction analysis shows an inclination of DF distribution from less to more urbanized areas. The results from the GWR show that among various socio-ecological factors, the temperature is the most significantly associated with the DF followed by vegetation and built-up area. While the results are important to understand the DF situation in the study area and have useful implications for public health planning, the proposed framework is flexible, replicable, and robust to be utilized in other similar regions, particularly in developing countries in the tropics and sub-tropics.

FOCUS: Forecasting COVID-19 in the United States

Infectious disease forecasting has been a useful tool for public health planning and messaging during the COVID-19 pandemic. In partnership with the CDC, the organizers of the COVID-19 Forecast Hub have created a mechanism for forecasters from academia, industry, and government organizations to submit weekly near-term predictions of COVID-19 targets in the United States. Here we describe our efforts to participate in the COVID-19 Forecast Hub through the Forecasting COVID-19 in the United States (FOCUS) project. The effort led to more than three months of weekly submissions and development of an automated pipeline to generate forecasts. The models used in FOCUS yielded forecasts that ranked relatively well in terms of precision and accuracy.

Time series models for prediction of Leptospirosis in different climate zones in Sri Lanka

In tropical countries such as Sri Lanka, where leptospirosis—a deadly disease with a high mortality rate—is endemic, prediction is required for public health planning and resource allocation. Routinely collected meteorological data may offer an effective means of making such predictions. This study included monthly leptospirosis and meteorological data from January 2007 to April 2019 from Sri Lanka. Factor analysis was used first with rainfall data to classify districts into meteorological zones. We used a seasonal autoregressive integrated moving average (SARIMA) model for univariate predictions and autoregressive distributed lag (ARDL) model for multivariable analysis of leptospirosis with monthly average rainfall, temperature, relative humidity (RH), solar radiation (SR) and the number of rainy days/month (RD). Districts were classified into wet (WZ) and dry (DZ) zones and highlands (HL) based on the factor analysis of rainfall data. The WZ had the highest leptospirosis incidence; there was no difference in the incidence between the DZ and HL. Leptospirosis was fluctuated positively with rainfall, RH and RD, whereas temperature and SR were fluctuated negatively. ARIMA(1,0,0)(0,1,1)12, ARIMA(1,0,0)(1,1,1)12, and ARIMA(0,1,1)(0,1,1)12 were the best univariate models for DZ, WZ, and HL, respectively. Despite its known association, rainfall was positively significant in the WZ only at lag 5 (P = 0.03) but was negatively associated at lag 2 and 3 (P = 0.04). RD was positively associated for all three zones. Temperature was positively associated at lag 0 for the WZ and HL (P < 0.009) and was negatively associated at lag 1 for the WZ (P = 0.01). There was no association with RH in contrast to previous studies. Based on altitude and rainfall data, meteorological variables could effectively predict the incidence of leptospirosis with different models for different climatic zones. These predictive models could be effectively used in public health planning purposes.

Implementing an Urban Public Health Observatory for (Near) Real-Time Surveillance for the COVID-19 Pandemic

The COVID-19 pandemic is broadly undercutting global health and economies, while disproportionally impacting socially disadvantaged populations. An impactful pandemic surveillance solution must draw from multi-dimensional integration of social determinants of health (SDoH) to contextually inform traditional epidemiological factors. In this article, we describe an Urban Public Health Observatory (UPHO) model which we have put into action in a mid-sized U.S. metropolitan region to provide near real-time analysis and dashboarding of ongoing COVID-19 conditions. Our goal is to illuminate associations between SDoH factors and downstream pandemic health outcomes to inform specific policy decisions and public health planning.

Adoption of local public health plan for better health and quality of life in Belgrade

Issue Development of public health plan for the City of Belgrade, facilitate multisectoral participation and encourage local government to incorporate public health planning into integrated planning framework, including funding. Description of the Problem According to Public Health Low, adopted in Serbia in 2016 and Public health strategy (2018), Council for Health, as a professional body of the City government, was obliged to prepare draft of the Plan. Members of the City Council are representatives from different sectors: health care, public health, private sector, child care, education and civil society. Based on data and information from relevant institutions and organizations, situation analysis and health profile of the City have been prepared during 2018. Results Public health plan for the City of Belgrade has been drafted for a time period from 2020 to 2026, aligned and within time frame of the National public health strategy. Plan included: mission, vision, objectives, activities, responsible institutions, funding sources and indicators for monitoring. Focus was on health promotion and empowerment of citizens to adopt healthy lifestyle as well on investment in environmental sustainability, poverty and inequalities reduction and minimizing risks to human health and well-being. On December 2019, Belgrade City Assembly adopted this document, with full responsibility for implementation and budgeting specific programs and projects from 2020. Lessons Multisectoral working group, with clear defined scope of work, supported by regulations, encouraged and managed by experts in the field, highly motivated to be creator of changes is prerequisite for successful and productive public health planning process. Key messages Public health planning enabled communication and cooperation among experts and decision makers and represented a whole-of-local government approach to public health. Document is used as a resource and model for the other cities and municipalities.