Hybridization of hierarchical clustering with persistent homology in assessing haze episodes between air quality monitoring stations.

Haze has been a major issue afflicting Southeast Asian countries, including Malaysia, for the past few decades. Hierarchical agglomerative cluster analysis (HACA) is commonly used to evaluate the spatial behavior between areas in which pollutants interact. Typically, using HACA, the Euclidean distance acts as the dissimilarity measure and air quality monitoring stations are grouped according to this measure, thus revealing the most polluted areas. In this study, a framework for the hybridization of the HACA technique is proposed by considering the topological similarity (Wasserstein distance) between stations to evaluate the spatial patterns of the affected areas by haze episodes. For this, a tool in the topological data analysis (TDA), namely, persistent homology, is used to extract essential topological features hidden in the dataset. The performance of the proposed method is compared with that of traditional HACA and evaluated based on its ability to categorize areas according to the exceedance level of the particulate matter (PM10). Results show that additional topological features have yielded better accuracy compared to without the case that does not consider topological features. The cluster validity indices are computed to verify the results, and the proposed method outperforms the traditional method, suggesting a practical alternative approach for assessing the similarity in air pollution behaviors based on topological characterizations.

The Causality and Uncertainty of the COVID-19 Pandemic to Bursa Malaysia Financial Services Index's Constituents.

Valued in hundreds of billions of Malaysian ringgit, the Bursa Malaysia Financial Services Index's constituents comprise several of the strongest performing financial constituents in Bursa Malaysia's Main Market. Although these constituents persistently reside mostly within the large market capitalization (cap), the existence of the individual constituent's causal influence or intensity relative to each other's performance during uncertain or even certain times is unknown. Thus, the key purpose of this paper is to identify and analyze the individual constituent's causal intensity, from early 2018 (pre-COVID-19) to the end of the year 2021 (post-COVID-19) using Granger causality and Schreiber transfer entropy. Furthermore, network science is used to measure and visualize the fluctuating causal degree of the source and the effected constituents. The results show that both the Granger causality and Schreiber transfer entropy networks detected patterns of increasing causality from pre- to post-COVID-19 but with differing causal intensities. Unexpectedly, both networks showed that the small- and mid-caps had high causal intensity during and after COVID-19. Using Bursa Malaysia's sub-sector for further analysis, the Insurance sub-sector rapidly increased in causality as the year progressed, making it one of the index's largest sources of causality. Even after removing large amounts of weak causal intensities, Schreiber transfer entropy was still able to detect higher amounts of causal sources from the Insurance sub-sector, whilst Granger causal sources declined rapidly post-COVID-19. The method of using directed temporal networks for the visualization of temporal causal sources is demonstrated to be a powerful approach that can aid in investment decision making.

Assessment of health service delivery parameters in Kano and Zamfara States, Nigeria.

BACKGROUND: In 2013, the Nigeria Federal Ministry of Health established a Master Health Facility List (MHFL) as recommended by WHO. Since then, some health facilities (HFs) have ceased functioning and new facilities were established. We updated the MHFL and assessed service delivery parameters in the Malaria Frontline Project implementing areas in Kano and Zamfara States. METHODS: We assessed all HFs in each of the 34 project local government areas (LGAs) between July and September 2017. Project staff administered a semi-structured questionnaire developed for this assessment to heads of HFs about the type of facility, category and number of staff working at the facility and to record geo-coordinates of facility. RESULTS: In the Kano State project area, 726 HFs were identified and geo-located: 31 were new facilities, 608 (84%), 116 (16%) and two (0.3%) were Primary Health Care (PHC), secondary and tertiary facilities respectively. Using the national definition, there were 710 (98%) functional facilities and 644 (91%) of these reported to the national health information platform, District Health Information System, version 2 (DHIS2). The Zamfara project area had 739 HFs: eight were new, 715 (97%), 22 (3.0%) and two (0.2%) PHCs, secondary and tertiary facilities respectively. There were 695 (94%) functional facilities with 656 (94%) of these reporting to DHIS2. Using national criteria for primary health care designation, only 95 (9%) of all PHCs in the two States met the minimum human resource requirements. CONCLUSION: Most HFs were functional and reported to DHIS2. A comprehensive MHFL having all the important parameters that should be established and updated regularly by authorities to make it more useful for health services administration and management. Most functional facilities are understaffed.

Improving health information system for malaria program management: Malaria Frontline Project lessons learned from Kano and Zamfara States, Nigeria, 2016-2019.

The U.S. Centers for Disease Control and Prevention in collaboration with the National Malaria Elimination Program and the African Field Epidemiology Network established the Malaria Frontline Project to provide innovative approaches to improve the malaria program implementation in Kano and Zamfara States, Nigeria. Innovative approaches such as malaria bulletin, malaria monitoring wall chart, conduct of ward level data validation meetings and malaria dashboard have helped improve the use of data for decision making at all levels. Innovative approaches deployed during the project implementation facilitated data analysis and a better understanding of malaria program performance and data utilization for decision making at all levels. These innovative approaches may improve malaria control program performance in Nigeria and other resource limited countries.

Using persistent homology as preprocessing of early warning signals for critical transition in flood.

Flood early warning systems (FLEWSs) contribute remarkably to reducing economic and life losses during a flood. The theory of critical slowing down (CSD) has been successfully used as a generic indicator of early warning signals in various fields. A new tool called persistent homology (PH) was recently introduced for data analysis. PH employs a qualitative approach to assess a data set and provide new information on the topological features of the data set. In the present paper, we propose the use of PH as a preprocessing step to achieve a FLEWS through CSD. We test our proposal on water level data of the Kelantan River, which tends to flood nearly every year. The results suggest that the new information obtained by PH exhibits CSD and, therefore, can be used as a signal for a FLEWS. Further analysis of the signal, we manage to establish an early warning signal for ten of the twelve flood events recorded in the river; the two other events are detected on the first day of the flood. Finally, we compare our results with those of a FLEWS constructed directly from water level data and find that FLEWS via PH creates fewer false alarms than the conventional technique.

An Early Warning System for Flood Detection Using Critical Slowing Down.

The theory of critical slowing down (CSD) suggests an increasing pattern in the time series of CSD indicators near catastrophic events. This theory has been successfully used as a generic indicator of early warning signals in various fields, including climate research. In this paper, we present an application of CSD on water level data with the aim of producing an early warning signal for floods. To achieve this, we inspect the trend of CSD indicators using quantile estimation instead of using the standard method of Kendall's tau rank correlation, which we found is inconsistent for our data set. For our flood early warning system (FLEWS), quantile estimation is used to provide thresholds to extract the dates associated with significant increases on the time series of the CSD indicators. We apply CSD theory on water level data of Kelantan River and found that it is a reliable technique to produce a FLEWS as it demonstrates an increasing pattern near the flood events. We then apply quantile estimation on the time series of CSD indicators and we manage to establish an early warning signal for ten of the twelve flood events. The other two events are detected on the first day of the flood.