Anderson S-J, Ghys PD, Ombam R, Hallett TB. HIV prevention where it is needed most: comparison of strategies for the geographical allocation of interventions. J Int. 2017;20(4):e25020.

Google Scholar 

Kedziora DJ, Stuart RM, Pearson J, Latypov A, Dierst-Davies R, Duda M, et al. Optimal allocation of HIV resources among geographical regions. BMC Public Health. 2019;19(1):1509.

Article  PubMed  PubMed Central  Google Scholar 

Grantham KL, Kerr CC, Wilson DP. Local responses to local epidemics for national impact need advanced spatially explicit tools. AIDS. 2016;30(9):1981.

Article  Google Scholar 

Meyer-Rath G, McGillen JB, Cuadros DF, Hallett TB, Bhatt S, Wabiri N, et al. Targeting the right interventions to the right people and places: the role of geospatial analysis in HIV program planning. AIDS. 2018;32(8):957.

Article  PubMed  Google Scholar 

Mweemba C, Hangoma P, Fwemba I, Mutale W, Masiye F. Estimating district HIV prevalence in Zambia using small-area estimation methods (SAE). Popul Health Metrics. 2022;20(1):8.

Article  Google Scholar 

Prabhu S, Mehta SH, McFall AM, Srikrishnan AK, Vasudevan CK, Lucas GM, et al. Role of geospatial mapping in the planning of HIV programs a case study from Southern India. Medicine. 2021;100(34):e2709.

Article  Google Scholar 

Mohammed JI, Comber A, Brunsdon C, (eds). Population estimation in small areas: combining dasymetric mapping with pycnophylactic interpolation. GIS Research UK (GISRUK) Conference; 2012 11th-13th April. Lancaster University.

Tomintz MN, García-Barrios VM. Location-allocation planning. In: Cockerham W, Dingwall R, Quah SR, editors. The Wiley Blackwell encyclopedia of health, illness, behavior and society. Hoboken: Wiley-Blackwell; 2014.

Google Scholar 

Entwisle B, Walsh R, Rindfuss R, Chamratritharong A. 1998. Land-use/land-cover and population dynamics National Research Council, Division of Behavioral and Social Sciences and Education Board on Environmental Change and Society Committee on the Human Dimensions of Global Change People and Pixels Linking Remote Sensing and Social Science. National Academies Press. Washington.

Tomintz MN, Barnett R. Geosimulation approach for filling the gap of non-response smoking data from the census 2013: A spatial analysis of census area unit geographies. New Zealand Geographer. 2018.

Eberth JM, McLain AC, Hong Y, Sercy E, Diedhiou A, Kilpatrick DJ. Estimating county-level tobacco use and exposure in South Carolina: a spatial model-based small area estimation approach. Ann Epidemiol. 2018;28(7):481–8.

Article  PubMed  Google Scholar 

Koh K, Grady SC, Darden JT, Vojnovic I. Adult obesity prevalence at the county level in the United States, 2000–2010: downscaling public health survey data using a spatial microsimulation approach. Spatial Spatio-Temp Epidemiol. 2018;26:153–64.

Article  Google Scholar 

Morrissey K, Clarke G, Williamson P, Daly A, O’Donoghue C. Mental illness in Ireland: simulating its geographical prevalence and the role of access to services. Environ Plann B Plann Des. 2015;42(2):338–53.

Article  Google Scholar 

Riva M, Smith DM. Generating small-area prevalence of psychological distress and alcohol consumption: validation of a spatial microsimulation method. Soc Psychiatry Psychiatr Epidemiol. 2012;47(5):745–55.

Article  PubMed  Google Scholar 

Zmölnig J, Tomintz MN, Clarke GP. 2016. Mapping the distribution of diabetes for public health support in Austria. gisScience–Die Zeitschrift fur Geoinformatik 3: 86–97.

Lucas AM, Wilson NL. Schooling, wealth, risky sexual behaviour, and HIV/AIDS in Sub-Saharan Africa. J Develop Stud. 2019;55(10):2177–92.

Article  Google Scholar 

Akinyemi YC. Spatio-temporal pattern and gender disparity of HIV/AIDS prevalence in Nigeria. Int Soc Sci J. 2016;66(221–222):303–22.

Article  Google Scholar 

Hajizadeh M, Sia D, Heymann SJ, Nandi A. Socioeconomic inequalities in HIV/AIDS prevalence in sub-Saharan African countries: evidence from the demographic health surveys. Int J Equity Health. 2014;13(1):18.

Article  PubMed  PubMed Central  Google Scholar 

Doyle AM, Mavedzenge SN, Plummer ML, Ross DA. The sexual behaviour of adolescents in sub-Saharan Africa: patterns and trends from national surveys. Trop Med Int Health. 2012;17(7):796–807.

Article  PubMed  Google Scholar 

Ghys PD, Garnett GP. Trends in hiv prevalence and sexual behaviour among young people aged 15–24 years in countries most affected by hiv. Sex Trans Infect. 2010;86(Suppl. 2):72–83.

Google Scholar 

UN Statistics Division. Official list of MDG indicators: department of economic and social affairs UN 2008. https://mdgs.un.org/unsd/mdg/Host.aspx?Content=Indicators/OfficialList.htm.

UNICEF. MICS5 indicator list: UNICEF. 2022. https://mics.unicef.org/tools?round=mics5.

UN Statistics Division. Global indicator framework for the Sustainable Development Goals and targets of the 2030 Agenda for Sustainable Development. In: Affairs DoEaS, editor.: United Nations; 2022.

Slaymaker E. A critique of international indicators of sexual risk behaviour. Sex Trans Infect. 2004;80(suppl 2):ii13.

Google Scholar 

Mmari K, Sabherwal S. A review of risk and protective factors for adolescent sexual and reproductive health in developing countries: an update. J Adolesc Health. 2013;53(5):562–72.

Article  PubMed  Google Scholar 

UNAIDS, United Nations General Assembly Special Session on HIV/AIDS. Monitoring the declaration of commitment on HIV/AIDS guidelines on construction of core indicators 2008 reporting. Geneva: World Health Organization; 2007.

Google Scholar 

National Bureau of Statistics and United Nations Children's Fund. Nigeria multiple indicator cluster survey 2016–2017. 2018. https://nigerianstat.gov.ng/download/670.

Mahmood T, Abbas F, Kumar R, Somrongthong R. Why under five children are stunted in Pakistan? A multilevel analysis of Punjab multiple indicator cluster survey (MICS-2014). BMC Public Health. 2020;20(1):952.

Article  PubMed  PubMed Central  Google Scholar 

Pezzulo C, Nilsen K, Carioli A, Tejedor-Garavito N, Hanspal SE, Hilber T, et al. Geographical distribution of fertility rates in 70 low-income, lower-middle-income, and upper-middle-income countries, 2010–16: a subnational analysis of cross-sectional surveys. Lancet Glob Health. 2021;9(6):e802–12.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Danert K, Healy A. Monitoring groundwater use as a domestic water source by urban households: analysis of data from lagos state, nigeria and sub-saharan africa with implications for policy and practice. Water. 2021;13(4):568.

Article  Google Scholar 

Clarke G, Harding A. Conclusions and future research directions Spatial microsimulation A reference guide for users. Berlin: Springer; 2013.

Google Scholar 

Harland K. microsimulation model user guide: flexible modelling framework national centre for research methods working Paper 06/13. 2013.

Evans AJ. Flexible modelling framework (FMF). v1.3 ed2014. p. A tool for the microsimulation component to link synthetic populations with the original microdata.

Lovelace R, Dumont M. Spatial microsimulation with R. Boca Raton: CRC Press; 2016.

Book  Google Scholar 

Edwards KL, Clarke G. SimObesity: combinatorial optimisation (deterministic) model a reference guide for users spatial microsimulation. Berlin: Springer; 2013.

Google Scholar 

Harland K, Heppenstall A, Smith D, Birkin MH. Creating realistic synthetic populations at varying spatial scales: a comparative critique of population synthesis techniques. J Artif Soc Soc Simul. 2012. https://doi.org/10.1564/jasss.1909.

Article  Google Scholar 

De Menten G, Dekkers G, Bryon G, Liégeois P, O’Donoghue C. Liam2: a new open source development tool for discrete-time dynamic microsimulation models. J Artif Soc Soc Simul. 2014;17(3):1–9.

Article  Google Scholar 

Kavroudakis D. sms: microdata for geographical analysis in R. J Stat Softw. 2015;68(2):1–23.

Article  Google Scholar 

Richiardi M, Richardson R. JAS-mine: a new platform for microsimulation and agent-based modelling. Int J Microsimul. 2017;10(1):106–34.

Article  Google Scholar 

Burden S, Steel D. Constraint choice for spatial microsimulation. Popul Space Place. 2016;22(6):568–83.

Article  Google Scholar 

Birkin M, Clarke G. The enhancement of spatial microsimulation models using geodemographics. Ann Reg Sci. 2012;49(2):515–32.

Article  Google Scholar 

Birkin M, Clarke M. Spatial microsimulation models: a review and a glimpse into the future population dynamics and projection methods. Berlin: Springer; 2011.

Google Scholar 

Freistein K, Mahlert B. The potential for tackling inequality in the sustainable development goals. Third World Quarterly. 2016;37(12):2139–55.

Article  Google Scholar 

O’Neill J, Tabish H, Welch V, Petticrew M, Pottie K, Clarke M, et al. Applying an equity lens to interventions: using PROGRESS ensures consideration of socially stratifying factors to illuminate inequities in health. J Clin Epidemiol. 2014;67(1):56–64.

Article  PubMed  Google Scholar 

Moon G, Quarendon G, Barnard S, Twigg L, Blyth B. Fat nation: deciphering the distinctive geographies of obesity in England. Soc Sci Med. 2007;65(1):20–31.

Article  PubMed  Google Scholar 

Smith DM, Clarke GP, Harland K. Improving the synthetic data generation process in spatial microsimulation models. Environ Plan A. 2009;41(5):1251–68.

Article  Google Scholar 

Abubakar EO, Cunningham N. 2022. A spatially enriched synthetic population developed by spatial microsimulation of 2016/2017 Multiple Indicator Cluster Survey microdata of Kogi State Nigeria. In: Abubakar EO. Draft version (eds). Harvard Dataverse. Kogi

Edwards KL, Clarke GP, Thomas J, Forman D. Internal and external validation of spatial microsimulation models: small area estimates of adult obesity. Appl Spat Anal Policy. 2011;4(4):281–300.

Article  Google Scholar 

Edwards KL, Tanton R. Validation of spatial microsimulation models spatial microsimulation: a reference guide for users. Berlin: Springer; 2013.

Google Scholar 

Lovelace R, Ballas D, Watson M. A spatial microsimulation approach for the analysis of commuter patterns: from individual to regional levels. J Transp Geogr. 2014;34:282–96.

Article  Google Scholar 

Morrissey K, O’Donoghue C. Validation issues and the spatial pattern of household income spatial microsimulation for rural policy analysis. Berlin: Springer; 2013. p. 87–102.

Book  Google Scholar 

Edwards KL, Clarke GP. The design and validation of a spatial microsimulation model of obesogenic environments for children in Leeds, UK: SimObesity. Soc Sci Med. 2009;69(7):1127–34.

Article  PubMed  Google Scholar 

Morrissey K, Clarke G, O’Donoghue C. Linking static spatial microsimulation modelling to meso-scale models: the relationship between access to GP services and long-term illness spatial microsimulation: a reference guide for users. Berlin: Springer; 2013.

Google Scholar 

de Walque D. Does education affect HIV status? evidence from five African countries. World Bank Econo Rev. 2009;23(2):209–33.

Article  Google Scholar 

Bärnighausen T, Tanser F, Gqwede Z, Mbizana C, Herbst K, Newell M-L. High HIV incidence in a community with high HIV prevalence in rural South Africa: findings from a prospective population-based study. AIDS. 2008;22(1):139.

Article  PubMed  Google Scholar 

Kasirye I. HIV/AIDS Sero-prevalence and Socio-economic status: evidence from Uganda. Afr Dev Rev. 2016;28(3):304–18.

Article  Google Scholar 

Kayeyi N, Fylkesnes K, Wiium N, Sandøy IF. Decline in sexual risk behaviours among Young People in Zambia (2000–2009): do neighbourhood contextual effects play a role? PLoS ONE. 2013;8(5): e64881.

Article  CAS