Evidence map›Paper›PMID 34538526›Full record

ArticleVaccine2021

Microplanning for designing vaccination campaigns in low-resource settings: A geospatial artificial intelligence-based framework.

Thiago Augusto Hernandes Rocha, Dante Grapiuna de Almeida, Arthi Shankar Kozhumam, Núbia Cristina da Silva, Erika Bárbara Abreu Fonseca Thomaz, Rejane Christine de Sousa Queiroz, Luciano de Andrade, Catherine Staton, João Ricardo Nickenig Vissoci

Abstract read
In one paragraph

Article in Vaccine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Review
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

9 authors.

Thiago Augusto Hernandes RochaDuke Global Health Institute, Duke University, Durham, NC, United States of America. Electronic address: tar29@duke.edu.
Dante Grapiuna de AlmeidaMEDOMAI technology information systems, Belo Horizonte, Minas Gerais, Brazil. Electronic address: dante@medomai.com.br.
Arthi Shankar KozhumamDuke Global Health Institute, Duke University, Durham, NC, United States of America. Electronic address: arthi.kozhumam@duke.edu.
Núbia Cristina da SilvaMethods, Analytics and Technology for Health (M.A.T.H) Consortium, Belo Horizonte, Minas Gerais, Brazil.
Erika Bárbara Abreu Fonseca ThomazDepartment of Public Health, Federal University of Maranhão, São Luís, Maranhão, Brazil. Electronic address: erika.barbara@ufma.br.
Rejane Christine de Sousa QueirozDepartment of Public Health, Federal University of Maranhão, São Luís, Maranhão, Brazil. Electronic address: queiroz.rejane@ufma.br.
Luciano de AndradeDepartment of Medicine, State University of Maringá, Maringá, Paraná, Brazil.. Electronic address: landrade@uem.br.
Catherine StatonDuke Global Health Institute, Duke University, Durham, NC, United States of America; Division of Emergency Medicine, Department of Surgery, Duke University School of Medicine, Duke University, North Carolina, United States of America. Electronic address: catherine.lynch@duke.edu.
João Ricardo Nickenig VissociDuke Global Health Institute, Duke University, Durham, NC, United States of America; Division of Emergency Medicine, Department of Surgery, Duke University School of Medicine, Duke University, North Carolina, United States of America. Electronic address: jnv4@duke.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Existing campaign-based healthcare delivery programs used for immunization often fall short of established health coverage targets due to a lack of accurate estimates for population size and location. A microplan, an integrated set of detailed planning components, can be used to identify this information to support programs such as equitable vaccination efforts. Here, we presents a series of steps necessary to create an artificial intelligence-based framework for automated microplanning, and our pilot implementation of this analysis tool across 29 countries of the Americas. Further, we describe our processes for generating a conceptual framework, creating customized catchment areas, and estimating up-to-date populations to support microplanning for health campaigns. Through our application of the present framework, we found that 68 million individuals across the 29 countries are within 5 km of a health facility. The number of health facilities analyzed ranged from 2 in Peru to 789 in Argentina, while the total population within 5 km ranged from 1,233 in Peru to 15,304,439 in Mexico. Our results demonstrate the feasibility of using this methodological framework to support the development of customized microplans for health campaigns using open-source data in multiple countries. The pandemic is demanding an improved capacity to generate successful, efficient immunization campaigns; we believe that the steps described here can increase the automation of microplans in low resource settings.

Indexed as

Artificial IntelligenceHealth PromotionArgentinaHumansImmunization ProgramsMexicoCoronavirusCOVID-19Health campaignMicroplanVaccinationVaccine

Identifiers

PMID34538526
PMCPMC8496523

What OpenQuestion holds

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LicenceCC BY
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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.