Early warning signals of malaria resurgence in Kericho, Kenya

Harris, Mallory; Hay, Simon I.; Drake, John M.

doi:10.1098/rsbl.2019.0713

Cited by 40 publications

(36 citation statements)

References 25 publications

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“…The prevalence and recurrence of epidemics and disasters in Africa should be the impetus for greater investment in preparedness. Disasters such as COVID-19 pandemic, 80 81 Ebola epidemic, 4 flooding in east and southern Africa, 82 ongoing floods and recurrent malaria epidemics in Kenya 83 have become common. Investments on measures such as early warning systems should be put into place to detect, respond and effectively contain these threats.…”

Section: Discussionmentioning

confidence: 99%

A vulnerability index for COVID-19: spatial analysis at the subnational level in Kenya

2020

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BackgroundResponse to the coronavirus disease 2019 (COVID-19) pandemic calls for precision public health reflecting our improved understanding of who is the most vulnerable and their geographical location. We created three vulnerability indices to identify areas and people who require greater support while elucidating health inequities to inform emergency response in Kenya.MethodsGeospatial indicators were assembled to create three vulnerability indices; Social VulnerabilityIndex (SVI), Epidemiological Vulnerability Index (EVI) and a composite of the two, that is, Social Epidemiological Vulnerability Index (SEVI) resolved at 295 subcounties in Kenya. SVI included 19 indicators that affect the spread of disease; socioeconomic deprivation, access to services and population dynamics, whereas EVI comprised 5 indicators describing comorbidities associated with COVID-19 severe disease progression. The indicators were scaled to a common measurement scale, spatially overlaid via arithmetic mean and equally weighted. The indices were classified into seven classes, 1–2 denoted low vulnerability and 6–7, high vulnerability. The population within vulnerabilities classes was quantified.ResultsThe spatial variation of each index was heterogeneous across Kenya. Forty-nine northwestern and partly eastern subcounties (6.9 million people) were highly vulnerable, whereas 58 subcounties (9.7 million people) in western and central Kenya were the least vulnerable for SVI. For EVI, 48 subcounties (7.2 million people) in central and the adjacent areas and 81 subcounties (13.2 million people) in northern Kenya were the most and least vulnerable, respectively. Overall (SEVI), 46 subcounties (7.0 million people) around central and southeastern were more vulnerable, whereas 81 subcounties (14.4 million people) were least vulnerable.ConclusionThe vulnerability indices created are tools relevant to the county, national government and stakeholders for prioritisation and improved planning. The heterogeneous nature of the vulnerability indices underpins the need for targeted and prioritised actions based on the needs across the subcounties.

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Section: Discussionmentioning

confidence: 99%

A vulnerability index for COVID-19: spatial analysis at the subnational level in Kenya

2020

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“…For instance, in time-series data following ancient abrupt climate shifts, EWSs can be identified before the critical transition took place [27]. Similarly, EWSs were seen in the resurgence of malaria in Kericho, Kenya [18].…”

Section: Introductionmentioning

confidence: 99%

Anticipating the novel coronavirus disease (COVID-19) pandemic

Kaur

Sarkar

Chowdhury

et al. 2020

Preprint

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The infectious novel coronavirus disease COVID-19 outbreak has been declared as a public health emergency of international concern, and later as an epidemic. To date, this outbreak has infected more than one million people and killed over fifty thousand people across the world. In most countries, the COVID-19 incidence curve rises sharply in a short span of time, suggesting a transition from a disease free (or low-burden disease) equilibrium state to a sustained infected (or high-burden disease) state. Such a transition from one stable state to another state in a relatively short span of time is often termed as a critical transition. Critical transitions can be, in general, successfully forecasted using many statistical measures such as return rate, variance and lag-1 autocorrelation. Here, we report an empirical test of this forecasting on the COVID-19 data sets for nine countries including India, China and the United States. For most of the data sets, an increase in autocorrelation and a decrease in return rate predict the onset of a critical transition. Our analysis suggests two key features in predicting the COVID-19 incidence curve for a specific country: a) the timing of strict social distancing and/or lockdown interventions implemented, and b) the fraction of a nation's population being affected by COVID-19 at the time of implementation of these interventions. Further, using satellite data of nitrogen dioxide which is emitted predominantly as a result of anthropogenic activities, as an indicator of lockdown policy, we find that in countries where the lockdown was implemented early and strictly have been successful in reducing the extent of transmission of the virus. These results hold important implications for designing effective strategies to control the spread of infectious pandemics. CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

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“…The prevalence and recurrence of epidemics and disasters in Africa should be the impetus for greater investment in preparedness. Disasters such as COVID-19 pandemic [67,68], Ebola epidemic [4], flooding in East and Southern Africa [69], ongoing floods and recurrent malaria epidemics in Kenya [70] have become common. Investments on measures such as early warning systems should be put into place to detect, respond and effectively contain these threats.…”

Section: Discussionmentioning

confidence: 99%

A vulnerability index for COVID-19: spatial analysis to inform equitable response in Kenya

Macharia

Joseph

Okiro

2020

Preprint

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Background: Response to the COVID-19 pandemic calls for precision public health reflecting our improved understanding of who is the most vulnerable and their geographical location. We created three vulnerability indices to identify areas and people who require greater support while elucidating health inequities to inform emergency response in Kenya. Methods: Geospatial indicators were assembled to create three vulnerability indices; social (SVI), epidemiological (EVI) and a composite of the two (SEVI) resolved at 295 sub-counties in Kenya. SVI included nineteen indicators that affect the spread of disease; socio-economic inequities, access to services and population dynamics while EVI comprised five indicators describing comorbidities associated with COVID-19 severe disease progression. The indicators were scaled to a common measurement scale, spatially overlaid via arithmetic mean and equally weighted. The indices were classified into seven classes, 1-2 denoted low-vulnerability and 6-7 high-vulnerability. The population within vulnerabilities classes was quantified. Results: The spatial variation of each index was heterogeneous across Kenya. Forty-nine north-western and partly eastern sub-counties (6.9 m people) were highly vulnerable while 58 sub-counties (9.7 m people) in western and central Kenya were the least vulnerable for SVI. For EVI, 48 sub-counties (7.2 m people) in central and the adjacent areas and 81 sub-counties (13.2 m people) in northern Kenya were the most and least vulnerable respectively. Overall (SEVI), 46 sub-counties (7.0 m people) around central and south-eastern were more vulnerable while 81 sub-counties (14.4 m people) that were least vulnerable. Conclusion: The vulnerability indices created are tools relevant to the county, national government and stakeholders for prioritization and improved planning especially in highly vulnerable sub-counties where cases have not been confirmed. The heterogeneous nature of the vulnerability highlights the need to address social determinants of health disparities, strengthen the health system and establish programmes to cushion against the negative effects of the pandemic.

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Early warning signals of malaria resurgence in Kericho, Kenya

Cited by 40 publications

References 25 publications

A vulnerability index for COVID-19: spatial analysis at the subnational level in Kenya

A vulnerability index for COVID-19: spatial analysis at the subnational level in Kenya

Anticipating the novel coronavirus disease (COVID-19) pandemic

A vulnerability index for COVID-19: spatial analysis to inform equitable response in Kenya

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