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Introduction: In middle and low income regions of the world, there are approximately 90% of cervical cancer deaths. Factors contributing to the striking disproportionate cancer burden in the developing countries as opposed to the developed countries may include lack of effective large scale preventive interventions such as Cytology based screening programs, Visual Inspection with acetic acid (VIA) that emphasize patient education, accessible diagnostic treatment centres and established vaccination programs especially among Most at risk populations like female sex workers (FSWs). Methodology: The study was conducted in Calabar, South-Southern Nigeria. Calabar is a port city near the Cameroon border. This is a cross sectional retrospective study among 124 Human Immune Virus (HIV)Positive female sex workers receiving ART services, in which secondary data was obtained from the Clinic Data repository for a periodspanning from October 2018- October 2019. Data analysis was done using Statistical Package for Social Sciences (SPSS) version 22. Result: 36.30% of the respondents are between the ages of 21-30 years, 78.20% had 0-4 sexual partners, and 76.60% of the respondents use condom. 89.50% were virally suppressed while 3.20% of the study population tested positive to cervical cancer. In the regression analysis, there were non-significant negative correlation linking Age, Parity groups and viral suppression to cervical cancer, and a positive non-significant correlation linking sex partners, smoking, previous STI and educational status to cervical cancer. However, a positive significant correlation was noted between family planning method and risk of cervical cancer. Conclusion: HIV-positive Nigerian FSWs are at marginally increased risk for cervical pre-cancer and cancer. The ‘screen and treat approach’ using VIA had been shown to be effective for the detection of cervical pre-cancer and cancer, and has shown to reduce loss to follow-up. This should be rapidly incorporated into HIV-treatment programs in resource-constraint settings.

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Emergency response preparedness increases disaster resilience and mitigates its possible impacts, mostly in public health emergencies. Prompt activation of these response plans and rapid optimization of delivery models and are essential for effective management of emergencies and disaster. In this paper, existing computational models and algorithms for routing deliveries and logistics during public health emergencies are identified. An overview of recent developments of optimization models and contributions, with emphasis on their applications in situations of uncertainties and unreliability, as obtainable in low-resource countries, is presented. Specific recent improvements in biologically-inspired and intelligent algorithms, genetic algorithms, and artificial immune systems techniques are surveyed. The research gaps are identified, and suggestions for potential future research concentrations are proffered.