RetroSearch Browse

Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://pubmed.ncbi.nlm.nih.gov/32087820/ below:

Preparedness and vulnerability of African countries against importations of COVID-19: a modelling study

. 2020 Mar 14;395(10227):871-877. doi: 10.1016/S0140-6736(20)30411-6. Epub 2020 Feb 20. Preparedness and vulnerability of African countries against importations of COVID-19: a modelling study Giulia Pullano², Francesco Pinotti³, Eugenio Valdano⁴, Chiara Poletto³, Pierre-Yves Boëlle³, Eric D'Ortenzio⁵, Yazdan Yazdanpanah⁵, Serge Paul Eholie⁶, Mathias Altmann⁷, Bernardo Gutierrez⁸, Moritz U G Kraemer⁹, Vittoria Colizza¹⁰

Affiliations

¹ Spatial Epidemiology Laboratory, Université Libre de Bruxelles, Brussels, Belgium; Fonds National de la Recherche Scientifiques, Brussels, Belgium.
² INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France; Sociology and Economics of Networks and Services Laboratory at Orange Experience Design Laboratory Chatillion, Paris, France.
³ INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France.
⁴ Center for Biomedical Modeling, The Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles, LA, USA.
⁵ Infection, Antimicrobials, Modelling, Evolution, INSERM, Université de Paris, Paris, France; Bichat Claude Bernard Hospital, Assistance publique-Hôpitaux de Paris, Paris, France.
⁶ Service des Maladies Infectieuses et Tropicales, Centre Hospitalier Universitaire de Treichville, Abidjan, Côte d'Ivoire; Département de Dermatologie-Infectiologie, Unité de Formation et de Recherche des Sciences Médicales, Université Félix Houphouet-Boigny, Abidjan, Côte d'Ivoire.
⁷ IDLIC-Maladies Infectieuses Dans Les Pays à Ressources Limitées, INSERM U1219, Bordeaux, France; Bordeaux Population Health, University of Bordeaux, Bordeaux, France.
⁸ Department of Zoology, University of Oxford, Oxford, UK.
⁹ Department of Zoology, University of Oxford, Oxford, UK; Harvard Medical School, Harvard University, Boston, MA, USA; Computational Epidemiology Group, Boston Children's Hospital, Boston, MA, USA.
¹⁰ INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France. Electronic address: vittoria.colizza@inserm.fr.

PMID: 32087820
PMCID: PMC7159277
DOI: 10.1016/S0140-6736(20)30411-6

Item in Clipboard

Preparedness and vulnerability of African countries against importations of COVID-19: a modelling study

Marius Gilbert et al. Lancet. 2020.

. 2020 Mar 14;395(10227):871-877. doi: 10.1016/S0140-6736(20)30411-6. Epub 2020 Feb 20. Authors Marius Gilbert¹, Giulia Pullano², Francesco Pinotti³, Eugenio Valdano⁴, Chiara Poletto³, Pierre-Yves Boëlle³, Eric D'Ortenzio⁵, Yazdan Yazdanpanah⁵, Serge Paul Eholie⁶, Mathias Altmann⁷, Bernardo Gutierrez⁸, Moritz U G Kraemer⁹, Vittoria Colizza¹⁰ Affiliations

¹ Spatial Epidemiology Laboratory, Université Libre de Bruxelles, Brussels, Belgium; Fonds National de la Recherche Scientifiques, Brussels, Belgium.
² INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France; Sociology and Economics of Networks and Services Laboratory at Orange Experience Design Laboratory Chatillion, Paris, France.
³ INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France.
⁴ Center for Biomedical Modeling, The Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles, LA, USA.
⁵ Infection, Antimicrobials, Modelling, Evolution, INSERM, Université de Paris, Paris, France; Bichat Claude Bernard Hospital, Assistance publique-Hôpitaux de Paris, Paris, France.
⁶ Service des Maladies Infectieuses et Tropicales, Centre Hospitalier Universitaire de Treichville, Abidjan, Côte d'Ivoire; Département de Dermatologie-Infectiologie, Unité de Formation et de Recherche des Sciences Médicales, Université Félix Houphouet-Boigny, Abidjan, Côte d'Ivoire.
⁷ IDLIC-Maladies Infectieuses Dans Les Pays à Ressources Limitées, INSERM U1219, Bordeaux, France; Bordeaux Population Health, University of Bordeaux, Bordeaux, France.
⁸ Department of Zoology, University of Oxford, Oxford, UK.
⁹ Department of Zoology, University of Oxford, Oxford, UK; Harvard Medical School, Harvard University, Boston, MA, USA; Computational Epidemiology Group, Boston Children's Hospital, Boston, MA, USA.
¹⁰ INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France. Electronic address: vittoria.colizza@inserm.fr.

PMID: 32087820
PMCID: PMC7159277
DOI: 10.1016/S0140-6736(20)30411-6

Item in Clipboard

Abstract

Background: The novel coronavirus disease 2019 (COVID-19) epidemic has spread from China to 25 countries. Local cycles of transmission have already occurred in 12 countries after case importation. In Africa, Egypt has so far confirmed one case. The management and control of COVID-19 importations heavily rely on a country's health capacity. Here we evaluate the preparedness and vulnerability of African countries against their risk of importation of COVID-19.

Methods: We used data on the volume of air travel departing from airports in the infected provinces in China and directed to Africa to estimate the risk of importation per country. We determined the country's capacity to detect and respond to cases with two indicators: preparedness, using the WHO International Health Regulations Monitoring and Evaluation Framework; and vulnerability, using the Infectious Disease Vulnerability Index. Countries were clustered according to the Chinese regions contributing most to their risk.

Findings: Countries with the highest importation risk (ie, Egypt, Algeria, and South Africa) have moderate to high capacity to respond to outbreaks. Countries at moderate risk (ie, Nigeria, Ethiopia, Sudan, Angola, Tanzania, Ghana, and Kenya) have variable capacity and high vulnerability. We identified three clusters of countries that share the same exposure to the risk originating from the provinces of Guangdong, Fujian, and the city of Beijing, respectively.

Interpretation: Many countries in Africa are stepping up their preparedness to detect and cope with COVID-19 importations. Resources, intensified surveillance, and capacity building should be urgently prioritised in countries with moderate risk that might be ill-prepared to detect imported cases and to limit onward transmission.

Funding: EU Framework Programme for Research and Innovation Horizon 2020, Agence Nationale de la Recherche.

PubMed Disclaimer

Figures
Figure 1

COVID-19 incidence in China as…

Figure 1

COVID-19 incidence in China as of Feb 11, 2020, and annual volume of…

Figure 1

COVID-19 incidence in China as of Feb 11, 2020, and annual volume of outflow passenger per airport Cumulative incidence was calculated as total number of confirmed cases per province divided by population of the province.

Figure 2

Global distribution of importation risk…

Figure 2

Global distribution of importation risk over human population density, distribution of the SPAR…

Figure 2

Global distribution of importation risk over human population density, distribution of the SPAR capacity, and IDVI Countries with no estimates of importation risk correspond to situations where the risk of entry was found to be negligible at the time of analysis. The values of MUS (not visible on the maps) are importation risk 4·5 × 10⁻⁴ , SPAR 65, and IDVI 64. AGO=Angola. BDI=Burundi. BEN=Benin. BFA=Burkina Faso. BWA=Botswana. CAF=Central African Republic. CIV=Côte d’Ivoire. CMR=Cameroon. COD=Democratic Republic of the Congo. COG=Republic of the Congo. COM=Comoros. CPV=Cape Verde. DJI=Djibouti. DZA=Algeria. EGY=Egypt. ERI=Eritrea. ESH=Western Sahara. ETH=Ethiopia. GAB=Gabon. GHA=Ghana. GIN=Guinea. GMB=Gambia. GNB=Guinea-Bissau. GNQ=Equatorial Guinea. IDVI=Infectious Disease Vulnerability Index. KEN=Kenya. LBR=Liberia. LBY=Libya. LSO=Lesotho. MAR=Morocco. MDG=Madagascar. MLI=Mali. MOZ=Mozambique. MRT=Mauritania. MUS=Mauritius. MWI=Malawi. NAM=Namibia. NER=Niger. NGA=Nigeria. RWA=Rwanda. SDN=Sudan. SEN=Senegal. SLE=Sierra Leone. SOM=Somalia. SPAR=State Party Self-Assessment Annual Reporting. SSD=South Sudan. STP=São Tomé and Príncipe. SWZ=eSwatini. TCD=Chad. TGO=Togo. TUN=Tunisia. TZA=Tanzania. UGA=Uganda. ZAF=South Africa. ZMB=Zambia. ZWE=Zimbabwe.

Figure 3

Importation risk as a function…

Figure 3

Importation risk as a function of the SPAR capacity and IDVI in Africa…

Figure 3

Importation risk as a function of the SPAR capacity and IDVI in Africa Area of circles is proportional to country population. The grey area represents the intervals of SPAR and IDVI values for the Organisation for Economic Co-operation and Development countries. AGO=Angola. BWA=Botswana. CIV=Côte d’Ivoire. CMR=Cameroon. COD=Democratic Republic of the Congo. DZA=Algeria. EGY=Egypt. ETH=Ethiopia. GHA=Ghana. GIN=Guinea. IDVI=Infectious Disease Vulnerability Index. KEN=Kenya. LSO=Lesotho. MAR=Morocco. MDG=Madagascar. MOZ=Mozambique. MUS=Mauritius. NGA=Nigeria. RWA=Rwanda. SDN=Sudan. SEN=Senegal. SPAR=State Party Self-Assessment Annual Reporting. TCD=Chad. TUN=Tunisia. TZA=Tanzania. UGA=Uganda. ZAF=South Africa. ZMB=Zambia. ZWE=Zimbabwe.

Figure 4

Cluster of countries sharing similar…

Figure 4

Cluster of countries sharing similar risk of importation from specific Chinese provinces Cluster…

Figure 4

Cluster of countries sharing similar risk of importation from specific Chinese provinces Cluster number 1: Algeria, Angola, Chad, Egypt, Ethiopia, Ghana, Guinea, Côte d’Ivoire, Kenya, Mauritius, Morocco, Nigeria, South Africa, Sudan, Tanzania, Uganda, Zambia, and Zimbabwe. Cluster number 2: Cameroon, Democratic Republic of the Congo, Madagascar, Mozambique, Rwanda, Senegal, and Tunisia. Cluster number 3: Botswana and Lesotho. Countries in grey were estimated to have a negligible risk of entry at the time of analysis.