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Abbott, S and Fyles, M (2021). martyn1fyles/HouseholdContactTracing. [Data Collection]. Github. https://github.com/martyn1fyles/HouseholdContactTracing

Aldridge, RW, Lewer, D, Beale, S, Johnson, AM, Zambon, M, Hayward, AC and Fragaszy, E (2020). Dataset: Seasonality and immunity to laboratory-confirmed seasonal coronaviruses (HCoV-NL63, HCoV-OC43, and HCoV-229E): results from the Flu Watch cohort study. [Data Collection]. UCL Institute of Health Informatics, London, United Kingdom. https://doi.org/10.14324/000.ds.10093909

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Berrang-Ford, L, Sietsma, AJ, Callaghan, M, Minx, J, Scheelbeek, P, Haddaway, N, Haines, A and Dangour, AD (2021). Dataset for article Berrang-Ford et al. "Systematic mapping of global research on climate and health using machine learning" The Lancet Planetary Health. [Data Collection]. Zenodo. https://doi.org/10.5281/zenodo.4972514

Bidulka, P (2021). England-Codelist-ICD10-Covariates. [Data Collection]. London School of Hygiene & Tropical Medicine, London, United Kingdom. https://doi.org/10.17037/DATA.00002256.

Bidulka, P (2021). England-Codelist-ICD10-Outcomes. [Data Collection]. London School of Hygiene & Tropical Medicine, London, United Kingdom. https://doi.org/10.17037/DATA.00002254.

Bidulka, P (2021). England-Codelist-ProductCodes-Covariates. [Data Collection]. London School of Hygiene & Tropical Medicine, London, United Kingdom. https://doi.org/10.17037/DATA.00002255.

Bosse, N, Abbott, S and Funk, S (2022). epiforecasts/covid.german.forecasts. [Data Collection]. Github. https://github.com/epiforecasts/covid.german.forecasts

Brady, OJ (2018). obrady/Brazil_microcepahly_analysis_public. [Data Collection]. Github. https://github.com/obrady/Brazil_microcepahly_analysis_public

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Cerqueira-Silva, T, de Araujo Oliveira, V, Paixão, ES, Júnior, JB, Penna, GO, Werneck, GL, Pearce, N, Barreto, ML, Boaventura, VS and Barral-Netto, M (2022). Duration of protection of CoronaVac plus heterologous BNT162b2 booster in the Omicron period in Brazil. [Data Collection]. Zenodo. https://doi.org/10.5281/zenodo.6657862

Chapman, LAC, Shukla, P and Lo, NC (2021). Comparison of COVID-19 vaccine prioritization strategies in the United States. [Data Collection]. Zenodo. https://doi.org/10.5281/zenodo.4516525

Costello, RE, Humphreys, JH, Sergeant, JC, Haris, M, Stirling, F, Raza, K, van Schaardenburg, D and Bruce, IN (2021). Additional file 1 of Symptoms in first-degree relatives of patients with rheumatoid arthritis: evaluation of cross-sectional data from the symptoms in persons at risk of rheumatoid arthritis (SPARRA) questionnaire in the PRe-clinical EValuation of Novel Targets in RA (PREVeNT-RA) Cohort. [Data Collection]. Figshare. https://doi.org/10.6084/m9.figshare.15153836.v1

Coutinho, C, Bastos, L, Corrêa da Mota, J, Toledo, L, Costa, K, Bertoni, N and Bastos, FI (2019). The risks of HCV infection among Brazilian crack cocaine users: incorporating diagnostic test uncertainty. [Data Collection]. Scientific Reports. https://doi.org/10.1038/s41598-018-35657-0

Cox, S and Faguer, B (2021). Patterns of non-communicable comorbidities at start of tuberculosis treatment in three regions of the Philippines: St-ATT cohort baseline data. [Data Collection]. London School of Hygiene & Tropical Medicine, London, United Kingdom. https://doi.org/10.17037/DATA.00002476.

D

Domman, D, Quilici, M, Dorman, MJ, Njamkepo, E, Mutreja, A, Mather, AE, Delgado, G, Morales-Espinosa, R, Grimont, PAD, Lizárraga-Partida, ML, Bouchier, C, Aanensen, DM, Kuri-Morales, P, Tarr, CL, Dougan, G, Parkhill, J, Campos, J, Cravioto, A, Weill, F and Thomson, NR (2017). Integrated view of Vibrio cholerae in the Americas. [Data Collection]. Figshare. https://doi.org/10.6084/m9.figshare.5427253.v1

F

Fernandez, MAL, Schomaker, M, Rachet, B and Schnitzer, ME (2018). Targeted maximum likelihood estimation for a binary treatment: A tutorial. [Data Collection]. Zenodo. https://doi.org/10.5281/zenodo.2560802

Fernandez, MAL, Zivich, P and Smith, MJ (2020). migariane/TutorialCausalInferenceEstimators: Computational Causal Inference for Applied Researchers. [Data Collection]. Zenodo. https://doi.org/10.5281/zenodo.4424912

Finch, E and Kucharski, AJ (2022). EmilieFinch/covid-reinfection. [Data Collection]. Github. https://github.com/EmilieFinch/covid-reinfection

G

Garcia, KKS, Soremekun, S, Bottomley, C, Abrahão, AA, de Miranda, CB, Drakeley, C, Ramalho, WM and Siqueira, AM (2023). Additional file 2 of Assessing the impact of the “malaria supporters project” intervention to malaria control in the Brazilian Amazon: an interrupted time-series analysis. [Data Collection]. Figshare. https://doi.org/10.6084/m9.figshare.24151173.v1

Garcia, KKS, Soremekun, S, Bottomley, C, Abrahão, AA, de Miranda, CB, Drakeley, C, Ramalho, WM and Siqueira, AM (2023). Additional file 3 of Assessing the impact of the “malaria supporters project” intervention to malaria control in the Brazilian Amazon: an interrupted time-series analysis. [Data Collection]. Figshare. https://doi.org/10.6084/m9.figshare.24151176.v1

Gibb, R (2023). rorygibb/dengue_vietnam_ms: v1.0.0: Code base for Nature Communications MS. [Data Collection]. Zenodo. https://doi.org/10.5281/zenodo.10159288

Grant, AD and Kielmann, K (2022). Qualitative and Quantitative Data for Tuberculosis Infection Prevention and Control in KwaZulu-Natal and Western Cape, South Africa, 2018-2021. [Data Collection]. UK Data Service, Colchester, Essex, United Kingdom. https://doi.org/10.5255/UKDA-SN-854435

H

Haque, F, Rahman, M, Banu, NN, Sharif, AR, Jubayer, S, Shamsuzzaman, A, Alamgir, A, Erasmus, JH, Guzman, H, Forrester, N, Luby, SP and Gurley, ES (2019). Syndromic survey de-identified data in SPSS. [Data Collection]. PLOS ONE. https://doi.org/10.1371/journal.pone.0212218.s005

Haque, F, Rahman, M, Banu, NN, Sharif, AR, Jubayer, S, Shamsuzzaman, A, Alamgir, A, Erasmus, JH, Guzman, H, Forrester, N, Luby, SP and Gurley, ES (2019). De-identified clinical survey data in SPSS. [Data Collection]. PLOS ONE. https://doi.org/10.1371/journal.pone.0212218.s006

Haque, F, Rahman, M, Banu, NN, Sharif, AR, Jubayer, S, Shamsuzzaman, A, Alamgir, A, Erasmus, JH, Guzman, H, Forrester, N, Luby, SP and Gurley, ES (2019). De-identified symptom duration data in SPSS. [Data Collection]. PLOS ONE. https://doi.org/10.1371/journal.pone.0212218.s007

J

Jombart, T, Cori, A, Didelot, X, Cauchemez, S, Fraser, C and Ferguson, N (2014). Bayesian Reconstruction of Disease Outbreaks by Combining Epidemiologic and Genomic Data. [Data Collection]. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1003457

K

Kalua, K and Manikavasagar, P (2024). Prevalence and causes of visual impairment among school children in Blantyre, Southern Malawi. [Data Collection]. Mendeley Data. https://doi.org/10.17632/crgbf93fnw.1

Klepac, P, Kucharski, AJ, Conlan, AJ, Kissler, S, Tang, M, Fry, H and Gog, JR (2020). Contacts in context: large-scale setting-specific social mixing matrices from the BBC Pandemic project. [Data Collection]. Medrxiv. https://doi.org/10.1101/2020.02.16.20023754

Koltai, M (2021). Source data for "Date of introduction and epidemiologic patterns of SARS-CoV-2 in Mogadishu, Somalia: estimates from transmission modelling of satellite-based excess mortality data in 2020". [Data Collection]. Zenodo. https://doi.org/10.5281/zenodo.5534768

Koltai, M and Krauer, F (2022). mbkoltai/RSV-resurgence-model. [Data Collection]. Github. https://github.com/mbkoltai/RSV-resurgence-model

Ku, C, MacPherson, P, Khundi, M, Nzawa Soko, RH, Feasey, HRA, Nliwasa, M, Horton, KC, Corbett, EL and Dodd, PJ (2021). Durations of asymptomatic, symptomatic, and care-seeking phases of tuberculosis disease with a Bayesian analysis of prevalence survey and notification data. [Data Collection]. London School of Hygiene & Tropical Medicine, London, United Kingdom. https://doi.org/10.17037/DATA.00002633.

Küpers, LK, Monnereau, C, Sharp, GC, Yousefi, P, Salas, LA, Ghantous, AG, Page, CM, Reese, SE, Wilcox, AJ, Czamara, D, Starling, AP, Novoloaca, A, Lent, S, Roy, R, Hoyo, C, Breton, CV, Allard, C, Just, AC, Bakulski, KM, Holloway, JW, Everson, TM, Xu, C, Huang, R, van der Plaat, DA, Wielscher, M, Kebede Merid, S, Ullemar, V, Rezwan, FI, Lahti, J, van Dongen, J, Langie, SA, Richardson, TG, Magnus, MC, Nohr, EA, Xu, Z, Duijts, L, Zhao, S, Zhang, W, Plusquin, M, DeMeo, DL, Solomon, O, Heimovaara, JH, Jima, DD, Gao, L, Bustamante, M, Perron, P, Wright, RO, Hertz-Picciotto, I, Zhang, H, Karagas, MR, Gehring, U, Marsit, CJ, Beilin, LJ, Vonk, JM, Jarvelin, M, Bergström, A, Örtqvist, AK, Ewart, S, Villa, PM, Moore, S, Willemsen, G, Standaert, AR, Håberg, SE, Sørensen, TI, Taylor, JA, Räikkönen, K, Yang, IV, Kechris, K, Nawrot, TS, Silver, MJ, Gong, YY, Richiardi, L, Kogevinas, M, Litonjua, AA, Eskenazi, B, Huen, K, Mbarek, H, Maguire, RL, Dwyer, T, Vrijheid, M, Bouchard, L, Baccarelli, AA, Croen, LA, Karmaus, W, Anderson, D, de Vries, M, Sebert, S, Kere, J, Karlsson, R, Hasan Arshad, S, Hämäläinen, E, Routledge, MN, Boomsma, DI, Feinberg, AP, Newschaffer, CJ, Govarts, E, Moisse, M, Fallin, MD, Melén, E, Prentice, AM, Kajantie, E, Almqvist, C, Oken, E, Dabelea, D, Boezen, HM, Melton, PE, Wright, RJ, Koppelman, GH, Trevisi, L, Hivert, M, Sunyer, J, Munthe-Kaas, MC, Murphy, SK, Corpeleijn, E, Wiemels, J, Holland, N, Herceg, Z, Binder, EB, Smith, GD, Jaddoe, VW, Lie, RT, Nystad, W, London, SJ, Lawlor, DA, Relton, CL, Snieder, H and Felix, JF (2019). Meta-analysis of epigenome-wide association studies in neonates reveals widespread differential DNA methylation associated with birthweight. [Data Collection]. Nature Communications. https://doi.org/10.1038/s41467-019-09671-3

L

Langdon, RJ, Yousefi, P, Relton, CL and Suderman, MJ (2021). Additional file 1 of Epigenetic modelling of former, current and never smokers. [Data Collection]. Figshare. https://doi.org/10.6084/m9.figshare.17037798.v1

Langdon, RJ, Yousefi, P, Relton, CL and Suderman, MJ (2021). Additional file 4 of Epigenetic modelling of former, current and never smokers. [Data Collection]. Figshare. https://doi.org/10.6084/m9.figshare.17037810.v1

Lim, A and Brady, OJ (2024). OpenDengue/master-repo. [Data Collection]. GitHub. https://github.com/OpenDengue/master-repo

le Polain de Waroux, O, Flasche, S, Kucharski, AJ, Langendorf, C, Ndazima, D, Mwanga-Amumpaire, J, Grais, RF, Cohuet, S and Edmunds, WJ (2018). Data for: Identifying human encounters that shape the transmission of Streptococcus pneumoniae and other acute respiratory infections. [Data Collection]. Mendeley Data. https://doi.org/10.17632/rdskdgxrh3.1

M

MacPherson, P, Khundi, M, Nliwasa, M, Choko, AT, Phiri, VK, Webb, EL, Dodd, PJ, Cohen, T, Harris, R and Corbett, EL (2018). BlantyreTBEpi. [Data Collection]. Github. https://github.com/petermacp/BlantyreTBEpi

Matthewman, J (2024). julianmatthewman/Eczema_outcomes_public. [Data Collection]. Github. https://github.com/julianmatthewman/Eczema_outcomes_public/

Mbivnjo, EL, Lynch, M and Huws, JC (2021). Supplementary material for the article, "Measles Outbreak Investigation Process in Low- and Middle-Income Countries: A Systematic Review of the Methods and Costs of Contact Tracing". [Data Collection]. Figshare. https://doi.org/10.6084/m9.figshare.14247431.v3

McQuaid, CF, Henrion, MYR, Burke, RM, MacPherson, P, Nzawa-Soko, R and Horton, KC (2022). Additional file 1 of Inequalities in the impact of COVID-19-associated disruptions on tuberculosis diagnosis by age and sex in 45 high TB burden countries. [Data Collection]. Figshare. https://doi.org/10.6084/m9.figshare.21550982.v1

Miranda, JJ, Bernabé-Ortiz, A and Carrillo-Larco, R (2021). PERU MIGRANT Study. [Data Collection]. Figshare. https://doi.org/10.6084/m9.figshare.16811350.v3

Motala, A, Adebamowo, C, Balde, N, Kaleebu, P, Kapiga, S, Levitt, N, Mayige, M, McCarthy, M, Nyirenda, M, Heyderman, R, Oli, J, Rotimi, C, Sandhu, M, Sobngwi, E, Smeeth, L and Kenneth, E (2021). The H3A Diabetes Study: A multi-centre study of the prevalence and environmental and genetic determinants of type 2 diabetes in sub-Saharan Africa. (Field Data collection 2015-2018). [Data Collection]. Africa Health Research Institute. https://doi.org/10.23664/H3A.CLINICAL.AND.POPULATION.DATASET.2021.VERSION.1

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Nkosi-Gondwe, T (2020). Moderate to severe malnutrition in severe anemia study. [Data Collection]. Figshare. https://doi.org/10.6084/m9.figshare.13061447.v1

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O'Reilly, KM (2022). kath-o-reilly/wbe_prevalence_england_python. [Data Collection]. Zenodo. https://doi.org/10.5281/zenodo.6674384

O’Hara, G, Mokaya, J, Hau, J, Downs, L, Karabarinde, A, Asiki, G, McNaughton, A, Seeley, J, Matthews, P and Newton, R (2021). Liver function tests and fibrosis scores in a rural population in Africa. [Data Collection]. Figshare. https://doi.org/10.6084/m9.figshare.8292194.v3

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Pullan, RL, Halliday, K, Oswald, W, Mcharo, C, Beaumont, E, Kepha, S, Witek-McManus, S, Gichuki, PM, Allen, E, Drake, T, Pitt, C, Matendechero, S, Gwayi-Chore, MC, Anderson, RM, Njenga, SM, Brooker, S and Mwandawiro, CS (2019). Household and Parasitology Surveys, Kwale County, Kenya, 2015-2017. [Data Collection]. London School of Hygiene & Tropical Medicine, London, United Kingdom. https://doi.org/10.17037/DATA.00001305.

Pung, R (2022). rachaelpung/cruise_networks. [Data Collection]. Zenodo. https://doi.org/10.5281/zenodo.6009026

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Rees, E (2022). erees/leptoSerology. [Data Collection]. Github. https://github.com/erees/leptoSerology

Regassa, LD, Gete, YK and Mekonnen, FA (2019). Time to acute kidney injury and its predictors among newly diagnosed Type 2 diabetic patients at government hospitals in Harari Region, East Ethiopia. S1 Data. [Data Collection]. PLOS ONE. https://doi.org/10.1371/journal.pone.0215967.s001

Rosello, A, Mossoko, M, Flasche, S, Vanhoek, A, Mbala, P, Camacho, A, Funk, S, Kucharski, A, Ilunga, BK, Edmunds, J, Piot, P, Baguelin, M and Tamfum, JM (2015). Ebola virus disease in the Democratic Republic of the Congo, 1976-2014: Figures and Data. [Data Collection]. London School of Hygiene & Tropical Medicine, London, United Kingdom. https://doi.org/10.17037/DATA.30.

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Sallum, MAM, Conn, JE, Bergo, ES, Laporta, GZ, Chaves, LS, Bickersmith, SA, de Oliveira, TM, Figueira, EAG, Moresco, G, Lêuda, O, Struchiner, CJ, Yakob, L and Massad, E (2019). Vector competence, vectorial capacity of Nyssorhynchus darlingi and the basic reproduction number of Plasmodium vivax in agricultural settlements in the Amazonian Region of Brazil. [Data Collection]. Figshare. https://doi.org/10.6084/m9.figshare.7956500.v1

Seeley, J, Mugisha, J and Suzman, R (2014). SAGE Well-Being of Older People Study-2013, Wave 2. [Data Collection]. WHO Multi-Country Studies Data Archive. https://apps.who.int/healthinfo/systems/surveydata/index.php/catalog/210

Simpson, KMJ, Mor, SM, Ward, MP, Collins, J, Flint, J, Hill-Cawthorne, GA and El Ghany, MA (2020). Dataset for "Genomic characterisation of Salmonella enterica serovar Wangata isolates obtained from different sources reveals low genomic diversity". [Data Collection]. PLOS ONE. https://doi.org/10.1371/journal.pone.0229697.s002

Sobkowiak, B, Glynn, JR, Houben, Rein M. G. J., Mallard, K, Phelan, JE, Guerra-Assunção, JA, Banda, L, Mzembe, T, Viveiros, M, McNerney, R, Parkhill, J, Crampin, AC and Clark, TG (2018). Additional file 1: of Identifying mixed Mycobacterium tuberculosis infections from whole genome sequence data. [Data Collection]. Figshare. https://doi.org/10.6084/m9.figshare.6968636.v1

Stockdale, L, Nash, S, Nalwoga, A, Painter, H, Asiki, G, Fletcher, H and Newton, R (2018). Data from: Human cytomegalovirus epidemiology and relationship to tuberculosis and cardiovascular disease risk factors in a rural Ugandan cohort. [Data Collection]. Dryad. https://doi.org/10.5061/dryad.d1k17

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UCLEB Consortium (2013). Population Genomics of Cardiometabolic Traits: Design of the UCL-LSHTM-Edinburgh-Bristol (UCLEB) Consortium. [Data Collection]. PLoS One. https://doi.org/10.1371/journal.pone.0071345

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Wang, SV, Sreedhara, SK, Schneeweiss, S and REPEAT Initiative (2021). Reproducible Evidence: Practices to Enhance and Achieve Transparency (REPEAT). [Data Collection]. OSF. https://doi.org/10.17605/OSF.IO/MY5GN

Wu, JT, Mei, S, Luo, S, Leung, K, Liu, D, Lv, Q, Liu, J, Li, Y, Prem, K, Jit, M, Weng, J, Feng, T, Zheng, X and Leung, GM (2021). Table S4 from A global assessment of the impact of school closure in reducing COVID-19 spread. [Data Collection]. Figshare. https://doi.org/10.6084/m9.figshare.16851696.v1