Identification of cholera hotspots in Zambia: A spatiotemporal analysis of cholera data from 2008 to 2017

Mwaba, John; Debes, Amanda K.; Shea, Patrick; Mukonka, Victor; Chewe, Orbrie; Chisenga, Caroline Cleopatra; Simuyandi, Michelo; Kwenda, Geoffrey; Sack, David A.; Chilengi, Roma; Ali, Mohammad

doi:10.1371/journal.pntd.0008227

Cited by 28 publications

(27 citation statements)

References 16 publications

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“…We infer that these outbreaks have different origins and must have entered independently into the country from across its borders. Consistent with our observation, on a local level, case-area targeted invention strategies [ 23 , 26 , 27 ] should be employed and at the international and regional level, well-coordinated plans should be put in place to effectively control future cholera outbreaks.…”

Section: Discussionsupporting

confidence: 72%

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Three transmission events of Vibrio cholerae O1 into Lusaka, Zambia

et al. 2021

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Background Cholera has been present and recurring in Zambia since 1977. However, there is a paucity of data on genetic relatedness and diversity of the Vibrio cholerae isolates responsible for these outbreaks. Understanding whether the outbreaks are seeded from existing local isolates or if the outbreaks represent separate transmission events can inform public health decisions. Results Seventy-two V. cholerae isolates from outbreaks in 2009/2010, 2016, and 2017/2018 in Zambia were characterized using multilocus variable number tandem repeat analysis (MLVA) and whole genome sequencing (WGS). The isolates had eight distinct MLVA genotypes that clustered into three MLVA clonal complexes (CCs). Each CC contained isolates from only one outbreak. The results from WGS revealed both clustered and dispersed single nucleotide variants. The genetic relatedness of isolates based on WGS was consistent with the MLVA, each CC was a distinct genetic lineage and had nearest neighbors from other East African countries. In Lusaka, isolates from the same outbreak were more closely related to themselves and isolates from other countries than to isolates from other outbreaks in other years. Conclusions Our observations are consistent with i) the presence of random mutation and alternative mechanisms of nucleotide variation, and ii) three separate transmission events of V. cholerae into Lusaka, Zambia. We suggest that locally, case-area targeted invention strategies and regionally, well-coordinated plans be in place to effectively control future cholera outbreaks.

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Section: Discussionsupporting

confidence: 72%

“…When our data were analyzed at the ward level within Lusaka, the capital city and an identified cholera hotspot [ 23 ], we observed that isolates from the 2016 outbreak came from eleven wards, while the 2017/18 isolates came from three wards (Fig. 2 ).…”

Section: Discussionmentioning

confidence: 99%

Three transmission events of Vibrio cholerae O1 into Lusaka, Zambia

et al. 2021

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“…These differences could result from the fact that we used data for a longer time period (10 years) with rigorous statistical methods and ignored mean annual incidence ≤ 2 cases per 100 000 people while the desk review data spanned six years using mean annual incidence of 1 case per 100 000 inhabitants. As we found in this study, SaTScan has been widely used and found very helpful in identification and classification of cholera clusters (hotspots) to inform intervention measures in SSA including Cameroon [ 17 ], Uganda [ 18 ], Zambia [ 46 ], and Tanzania [ 47 ]. However, the GTFCC method is relatively new and we found no evidence of its use in published reports.…”

Section: Discussionmentioning

confidence: 99%

The cholera risk assessment in Kano State, Nigeria: A historical review, mapping of hotspots and evaluation of contextual factors

Ngwa

Ihekweazu²,

Okwor³

et al. 2021

PLoS Negl Trop Dis

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Nigeria is endemic for cholera since 1970, and Kano State report outbreaks annually with high case fatality ratios ranging from 4.98%/2010 to 5.10%/2018 over the last decade. However, interventions focused on cholera prevention and control have been hampered by a lack of understanding of hotspot Local Government Areas (LGAs) that trigger and sustain yearly outbreaks. The goal of this study was to identify and categorize cholera hotspots in Kano State to inform a national plan for disease control and elimination in the State. We obtained LGA level confirmed and suspected cholera data from 2010 to 2019 from the Nigeria Centre for Disease Control (NCDC) and Kano State Ministry of Health. Data on inland waterbodies and population numbers were obtained from online sources and NCDC, respectively. Clusters (hotspots) were identified using SaTScan through a retrospective analysis of the data for the ten-year period using a Poisson discrete space-time scan statistic. We also used a method newly proposed by the Global Task Force on Cholera Control (GTFCC) to identify and rank hotspots based on two epidemiological indicators including mean annual incidence per 100 000 population of reported cases and the persistence of cholera for the study period. In the ten-year period, 16,461 cholera cases were reported with a case fatality ratio of 3.32% and a mean annual incidence rate of 13.4 cases per 100 000 population. Between 2010 and 2019, the most severe cholera exacerbations occurred in 2014 and 2018 with annual incidence rates of 58.01 and 21.52 cases per 100 000 inhabitants, respectively. Compared to 2017, reported cases and deaths increased by 214.56% and 406.67% in 2018. The geographic distribution of outbreaks revealed considerable spatial heterogeneity with the widest in 2014. Space-time clustering analysis identified 18 out of 44 LGAs as high risk for cholera (hotspots) involving both urban and rural LGAs. Cholera clustered around water bodies, and the relative risk of having cholera inside the hotspot LGA were 1.02 to 3.30 times higher than elsewhere in the State. A total of 4,894,144 inhabitants were in these hotspots LGAs. Of these, six LGAs with a total population of 1.665 million had a relative risk greater than 2 compared to the state as a whole. The SaTScan (statistical) and GTFCC methods were in agreement in hotspots identification. This study identified cholera hotspots LGAs in Kano State from 2010–2019. Hotspots appeared in both urban and rural settings. Focusing control strategies on these hotspots will facilitate control and eliminate cholera from the State.

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“…Spatiotemporal data can be analysed for building an interactive view of data points in similar and different regions, to analyse data from localized areas or to map and compare spatial distribution in individual regions [59]. In previous pandemics, spatiotemporal analysis has been used to identify cholera hotspots in Zambia [60]. With COVID-19 spreading so fast, the scientific peer-review process is struggling to catch up.…”

Section: Types Of Digital Data In Pandemic Managementmentioning

confidence: 99%

COVID-19 what have we learned? The rise of social machines and connected devices in pandemic management following the concepts of predictive, preventive and personalized medicine

et al. 2020

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Objectives Review, compare and critically assess digital technology responses to the COVID-19 pandemic around the world. The specific point of interest in this research is on predictive, preventive and personalized interoperable digital healthcare solutions. This point is supported by failures from the past, where the separate design of digital health solutions has led to lack of interoperability. Hence, this review paper investigates the integration of predictive, preventive and personalized interoperable digital healthcare systems. The second point of interest is the use of new mass surveillance technologies to feed personal data from health professionals to governments, without any comprehensive studies that determine if such new technologies and data policies would address the pandemic crisis. Method This is a review paper. Two approaches were used: A comprehensive bibliographic review with R statistical methods of the COVID-19 pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles and other reliable data sources. Results Most countries' digital responses involve big data analytics, integration of national health insurance databases, tracing travel history from individual's location databases, code scanning and individual's online reporting. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy. We propose predictive, preventive and personalized solutions for pandemic management, based on social machines and connected devices. Solutions The proposed predictive, preventive and personalized solutions are based on the integration of IoT data, wearable device data, mobile apps data and individual data inputs from registered users, operating as a social machine with strong security and privacy protocols. We present solutions that would enable much greater speed in future responses. These solutions are enabled by the social aspect of human-computer interactions (social machines) and the increased connectivity of humans and devices (Internet of Things). Conclusion Inadequate data for risk assessment on speed and urgency of COVID-19, combined with increased globalization of human society, led to the rapid spread of COVID-19. Despite an abundance of digital methods that could be used in slowing or stopping COVID-19 and future pandemics, the world remains unprepared, and lessons have not been learned from previous cases of pandemics. We present a summary of predictive, preventive and personalized digital methods that could be deployed fast to help with the COVID-19 and future pandemics.

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Identification of cholera hotspots in Zambia: A spatiotemporal analysis of cholera data from 2008 to 2017

Cited by 28 publications

References 16 publications

Three transmission events of Vibrio cholerae O1 into Lusaka, Zambia

Three transmission events of Vibrio cholerae O1 into Lusaka, Zambia

The cholera risk assessment in Kano State, Nigeria: A historical review, mapping of hotspots and evaluation of contextual factors

COVID-19 what have we learned? The rise of social machines and connected devices in pandemic management following the concepts of predictive, preventive and personalized medicine

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