Evidence map›Paper›PMID 41938840›Full record

ArticleEClinicalMedicine2026

A predictive, multi-source, attack-level model to quantify and characterize the injury burden and need for reconstructive surgery in Gaza.

Pranav N Haravu, Elaine Lin, Oumaima Berrada, Isha Chugh, Cooper Ruffing, Emily Song, Muskaan Toshniwal, Rohil Watwe, Victoria Rose, Catherine Staton and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EClinicalMedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Pranav N HaravuDivision of Plastic, Maxillofacial, and Oral Surgery, Duke University Medical Center, Durham, NC, USA.
Elaine LinDuke University School of Medicine, Durham, NC, USA.
Oumaima BerradaDuke University, Durham, NC, USA.
Isha ChughDuke University, Durham, NC, USA.
Cooper RuffingDuke University, Durham, NC, USA.
Emily SongDuke University, Durham, NC, USA.
Muskaan ToshniwalDuke University, Durham, NC, USA.
Rohil WatweDuke University, Durham, NC, USA.
Victoria RoseDepartment of Plastic and Reconstructive Surgery, Guy's and St. Thomas' NHS Foundation Trust, London, UK.
Catherine StatonDepartment of Emergency Medicine, Duke University Medical Center, Durham, NC, USA.
Ash PatelDivision of Plastic, Maxillofacial, and Oral Surgery, Duke University Medical Center, Durham, NC, USA.
Frances S HassoDuke Global Health Institute, Duke University, Durham, NC, USA.
Ahmed MokhallalatiDepartment of Burn and Plastic Surgery, Al Shifa Hospital, Gaza, PS, Palestine.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Since October 2023, the war in Gaza has produced a massive Palestinian civilian injury burden and created a large need for reconstructive surgery. It has also crippled an already fragile healthcare system and reduced its capacity to provide reconstructive care. Effective planning to address this gap requires a consensus on the volume and pattern of injuries, and the ability to forecast future injuries. However, acquiring accurate granular data is challenging in conflict settings. This study computationally estimates and characterizes injuries in Gaza to serve as a comparison to reported figures, forecast future injuries, and aid in planning to meet reconstructive needs. Methods: A multivariate negative binomial regression model was built using attack data, geospatial mapping, dynamic population density estimates and evolving infrastructure classifications from humanitarian, governmental, and media sources. The model was trained on data from October 2023-March 2024, validated in April 2024, and tested from May 2024-May 2025. The primary outcome was daily injury counts in Gaza, modeled as a function of attack counts, attack types, population density, infrastructure, and geographically moving areas of conflict. Forecasts through May 2026 were generated under varying trajectories of conflict intensity. Findings: For October 7th, 2023-May 1st, 2025, our model predicted 116,020 injuries (10% sensitivity analysis: 108,000-129,000), aligning with the 118,014 reported by the Gaza Ministry of Health (MoH). Injuries were greatest in air and shelling attacks, during periods of actively moving conflict, and in densely populated areas, especially in urban settings before and after they were devastated to rubble. Of those injuries, 29,000-46,000 were predicted to require reconstructive surgery, with over 80% due to explosions, and rising to 34,000-48,000 by May 2026. Model predictions were correlated with observed outcomes, with Spearman's ρ = 0.723 (p = 7.06 × 10 Interpretation: To our knowledge this is the first predictive model to forecast the injury burden in Gaza based on attack characteristics using granular multi-source data. Our findings corroborate figures published by the Gaza MoH and demonstrate that the burden will continue to grow without cessation of hostilities, further exacerbating reconstructive surgical need. Funding: Bass Connections grant, Duke University.

Indexed as

Conflict medicineGaza StripPredictive modelingReconstructive surgeryWar-related traumatic injuries

Identifiers

PMID41938840
PMCPMC13043315

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.