Evidence map›Paper›PMID 31104602›Full record

ArticlePhilosophical transactions of the Royal Society of London. Series B, Biological sciences2019

New methodologies for the estimation of population vulnerability to diseases: a case study of Lassa fever and Ebola in Nigeria and Sierra Leone.

Olumayowa Kajero, Victor Del Rio Vilas, James L N Wood, Giovanni Lo Iacono

Abstract read
In one paragraph

Article in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Lassa Fever: An Evolving Emergency in West Africa.The American journal of tropical medicine and hygiene · 2020
    Review
  3. Preface to theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019
    Article
  4. Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Olumayowa Kajero1 School of Veterinary Medicine, Daphne Jackson Road, University of Surrey , Guildford, Surrey GU2 7AL , UK.
Victor Del Rio Vilas1 School of Veterinary Medicine, Daphne Jackson Road, University of Surrey , Guildford, Surrey GU2 7AL , UK.
James L N Wood2 Department of Veterinary Medicine, Disease Dynamics Unit, University of Cambridge , Cambridge CB3 0ES , UK.
Giovanni Lo Iacono1 School of Veterinary Medicine, Daphne Jackson Road, University of Surrey , Guildford, Surrey GU2 7AL , UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Public health practitioners require measures to evaluate how vulnerable populations are to diseases, especially for zoonoses (i.e. diseases transmitted from animals to humans) given their pandemic potential. These measures would be valuable to support strategic and operational decision making and allocation of resources. Although vulnerability is well defined for natural hazards, for public health threats the concept remains undetermined. Here, we develop new methodologies to: (i) quantify the impact of zoonotic diseases and the capacity of countries to cope with these diseases, and (ii) combine these two measures (impact and capacity) into one overall vulnerability indicator. The adaptive capacity is calculated from estimations of disease mortality, although the method can be adapted for diseases with no or low mortality but high morbidity. As an example, we focused on the vulnerability of Nigeria and Sierra Leone to Lassa Fever and Ebola. We develop a simple analytical form that can be used to estimate vulnerability scores for different spatial units of interest, e.g. countries or regions. We show how some populations can be highly vulnerable despite low impact threats. We finally outline future research to more comprehensively inform vulnerability with the incorporation of relevant factors depicting local heterogeneities (e.g. bio-physical and socio-economic factors). This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

Indexed as

Models, BiologicalDisease SusceptibilityHemorrhagic Fever, EbolaHumansLassa FeverNigeriaRisk FactorsSierra Leoneadaptive capacityimpactmathematical modellingzoonosis

Identifiers

PMID31104602
PMCPMC6558567

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.