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Lemaitre JC, Pasetto D, Zanon M, Bertuzzo E, Mari L, Miccoli S, Casagrandi R, Gatto M, Rinaldo A. Optimal control of the spatial allocation of COVID-19 vaccines: Italy as a case study. PLoS Comput Biol 2022; 18:e1010237. [PMID: 35802755 PMCID: PMC9299324 DOI: 10.1371/journal.pcbi.1010237] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/20/2022] [Accepted: 05/23/2022] [Indexed: 12/16/2022] Open
Abstract
While campaigns of vaccination against SARS-CoV-2 are underway across the world, communities face the challenge of a fair and effective distribution of a limited supply of doses. Current vaccine allocation strategies are based on criteria such as age or risk. In the light of strong spatial heterogeneities in disease history and transmission, we explore spatial allocation strategies as a complement to existing approaches. Given the practical constraints and complex epidemiological dynamics, designing effective vaccination strategies at a country scale is an intricate task. We propose a novel optimal control framework to derive the best possible vaccine allocation for given disease transmission projections and constraints on vaccine supply and distribution logistics. As a proof-of-concept, we couple our framework with an existing spatially explicit compartmental COVID-19 model tailored to the Italian geographic and epidemiological context. We optimize the vaccine allocation on scenarios of unfolding disease transmission across the 107 provinces of Italy, from January to April 2021. For each scenario, the optimal solution significantly outperforms alternative strategies that prioritize provinces based on incidence, population distribution, or prevalence of susceptibles. Our results suggest that the complex interplay between the mobility network and the spatial heterogeneities implies highly non-trivial prioritization strategies for effective vaccination campaigns. Our work demonstrates the potential of optimal control for complex and heterogeneous epidemiological landscapes at country, and possibly global, scales.
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Affiliation(s)
- Joseph Chadi Lemaitre
- Laboratory of Ecohydrology, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari Venezia, Venezia-Mestre, Italy
| | - Damiano Pasetto
- Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari Venezia, Venezia-Mestre, Italy
| | | | - Enrico Bertuzzo
- Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari Venezia, Venezia-Mestre, Italy
| | - Lorenzo Mari
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Stefano Miccoli
- Dipartimento di Meccanica, Politecnico di Milano, Milan, Italy
| | - Renato Casagrandi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Marino Gatto
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Andrea Rinaldo
- Laboratory of Ecohydrology, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Dipartimento ICEA, Università di Padova, Padova, Italy
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Spassiani I, Sebastiani G, Palù G. Spatiotemporal Analysis of COVID-19 Incidence Data. Viruses 2021; 13:463. [PMID: 33799900 PMCID: PMC8001833 DOI: 10.3390/v13030463] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 03/05/2021] [Accepted: 03/08/2021] [Indexed: 01/08/2023] Open
Abstract
(1) Background: A better understanding of COVID-19 dynamics in terms of interactions among individuals would be of paramount importance to increase the effectiveness of containment measures. Despite this, the research lacks spatiotemporal statistical and mathematical analysis based on large datasets. We describe a novel methodology to extract useful spatiotemporal information from COVID-19 pandemic data. (2) Methods: We perform specific analyses based on mathematical and statistical tools, like mathematical morphology, hierarchical clustering, parametric data modeling and non-parametric statistics. These analyses are here applied to the large dataset consisting of about 19,000 COVID-19 patients in the Veneto region (Italy) during the entire Italian national lockdown. (3) Results: We estimate the COVID-19 cumulative incidence spatial distribution, significantly reducing image noise. We identify four clusters of connected provinces based on the temporal evolution of the incidence. Surprisingly, while one cluster consists of three neighboring provinces, another one contains two provinces more than 210 km apart by highway. The survival function of the local spatial incidence values is modeled here by a tapered Pareto model, also used in other applied fields like seismology and economy in connection to networks. Model's parameters could be relevant to describe quantitatively the epidemic. (4) Conclusion: The proposed methodology can be applied to a general situation, potentially helping to adopt strategic decisions such as the restriction of mobility and gatherings.
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Affiliation(s)
- Ilaria Spassiani
- Istituto Nazionale di Geofisica e Vulcanologia, Via di Vigna Murata 605, 00143 Rome, Italy;
| | - Giovanni Sebastiani
- Istituto Nazionale di Geofisica e Vulcanologia, Via di Vigna Murata 605, 00143 Rome, Italy;
- Istituto per le Applicazioni del Calcolo Mauro Picone, Consiglio Nazionale delle Ricerche, Via dei Taurini 19, 00185 Rome, Italy
- Mathematics Department “Guido Castelnuovo”, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
- Department of Mathematics and Statistics, University of Tromsø, H. Hansens veg 18, 9019 Tromsø, Norway
| | - Giorgio Palù
- Department of Molecular Medicine, University of Padua, Via Gabelli 63, 35121 Padua, Italy;
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Ditekemena JD, Nkamba DM, Mutwadi A, Mavoko HM, Siewe Fodjo JN, Luhata C, Obimpeh M, Van Hees S, Nachega JB, Colebunders R. COVID-19 Vaccine Acceptance in the Democratic Republic of Congo: A Cross-Sectional Survey. Vaccines (Basel) 2021; 9:153. [PMID: 33672938 PMCID: PMC7917589 DOI: 10.3390/vaccines9020153] [Citation(s) in RCA: 78] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 02/06/2021] [Accepted: 02/08/2021] [Indexed: 01/07/2023] Open
Abstract
We investigated the level of willingness for COVID-19 vaccination in the Democratic Republic of Congo (DRC). Data were collected between 24 August 2020 and 8 September 2020 through an online survey. A total of 4131 responses were included; mean age of respondents was 35 years (standard deviation: 11.5); 68.4% were females; 71% had elementary or secondary school education. One fourth (24.1%) were convinced that COVID-19 did not exist. Overall, 2310 (55.9%) indicated they were willing to be vaccinated. In a multivariable regression model, belonging to the middle and high-income category (OR = 1.85, CI: 1.46-2.35 and OR = 2.91, CI: 2.15-3.93, respectively), being tested for COVID-19 (OR = 4.71, CI: 3.62-6.12; p < 0.001), COVID-19 community vaccine acceptance (OR = 14.45, CI: 2.91-71.65; p = 0.001) and acknowledging the existence of COVID-19 (OR = 6.04, CI: 4.42-8.23; p < 0.001) were associated with an increased willingness to be vaccinated. Being a healthcare worker was associated with a decreased willingness for vaccination (OR = 0.46, CI: 0.36-0.58; p < 0.001). In conclusion, the current willingness for COVID-19 vaccination among citizens of the DRC is too low to dramatically decrease community transmission. Of great concern is the low intention of immunization among healthcare workers. A large sensitization campaign will be needed to increase COVID-19 vaccine acceptance.
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Affiliation(s)
- John D. Ditekemena
- Kinshasa School of Public Health, University of Kinshasa, Kinshasa 7948, Democratic Republic of the Congo; (D.M.N.); (A.M.); (C.L.)
| | - Dalau M. Nkamba
- Kinshasa School of Public Health, University of Kinshasa, Kinshasa 7948, Democratic Republic of the Congo; (D.M.N.); (A.M.); (C.L.)
- Pôle d’Épidémiologie et Biostatistique, Institut de Recherche Expérimentale et Clinique (IREC), Université Catholique de Louvain (UCLouvain), 1348 Brussels, Belgium
| | - Armand Mutwadi
- Kinshasa School of Public Health, University of Kinshasa, Kinshasa 7948, Democratic Republic of the Congo; (D.M.N.); (A.M.); (C.L.)
| | - Hypolite M. Mavoko
- Department of Tropical Medicine, University of Kinshasa, Kinshasa 7948, Democratic Republic of the Congo;
| | - Joseph Nelson Siewe Fodjo
- Global Health Institute, University of Antwerp, 2000 Antwerp, Belgium; (J.N.S.F.); (M.O.); (S.V.H.); (R.C.)
| | - Christophe Luhata
- Kinshasa School of Public Health, University of Kinshasa, Kinshasa 7948, Democratic Republic of the Congo; (D.M.N.); (A.M.); (C.L.)
| | - Michael Obimpeh
- Global Health Institute, University of Antwerp, 2000 Antwerp, Belgium; (J.N.S.F.); (M.O.); (S.V.H.); (R.C.)
| | - Stijn Van Hees
- Global Health Institute, University of Antwerp, 2000 Antwerp, Belgium; (J.N.S.F.); (M.O.); (S.V.H.); (R.C.)
| | - Jean B. Nachega
- Department of Epidemiology Infectious Diseases and Microbiology and Center for Global Health, University of Pittsburgh, Pittsburgh, PA 15260, USA;
- Departments of Epidemiology and International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Department of Medicine and Center for Infectious Diseases, Stellenbosch University Faculty of Medicine and Health Sciences, Cape Town 7505, South Africa
| | - Robert Colebunders
- Global Health Institute, University of Antwerp, 2000 Antwerp, Belgium; (J.N.S.F.); (M.O.); (S.V.H.); (R.C.)
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