Background The Human Immunodeficiency Virus(HIV) infection prevalence in Cameroon has decreased from $$5.28\%$$ 5.28 % in 2004 to $$2.8\%$$ 2.8 % in 2018. However, this decrease in prevalence does not show disparities especially in terms of spatial or geographical pattern. Efficient control and fight against HIV infection may require targeting hotspot areas. This study aims at presenting a cartography of HIV infection situation in Cameroon using the 2004, 2011 and 2018 Demographic and Health Survey data, and investigating whether there exist spatial patterns of the disease, may help to detect hot-spots. Methods HIV biomarkers data and Global Positioning System (GPS) location data were obtained from the Cameroon 2004, 2011, and 2018 Demographic and Health Survey (DHS) after an approved request from the MEASURES Demographic and Health Survey Program. HIV prevalence was estimated for each sampled area. The Moran’s I (MI) test was used to assess spatial autocorrelation. Spatial interpolation was further performed to estimate the prevalence in all surface points. Hot-spots were identified based on Getis–Ord (Gi*) spatial statistics. Data analyses were done in the R software(version 4.1.2), while Arcgis Pro software tools’ were used for all spatial analyses. Results Generally, spatial autocorrelation of HIV infection in Cameroon was observed across the three time periods of 2004 ($$MI=0.84$$ M I = 0.84 , $$p-value < 0.001$$ p - v a l u e < 0.001 ), 2011 ($$MI=0.80$$ M I = 0.80 , $$p-value < 0.001$$ p - v a l u e < 0.001 ) and 2018 ($$MI=0.87$$ M I = 0.87 , $$p-value < 0.001$$ p - v a l u e < 0.001 ). Subdivisions in which one could find persistent hot-spots for at least two periods including the last period 2018 included: Mbéré, Lom et Djerem, Kadey, Boumba et Ngoko, Haute Sanaga, Nyong et Mfoumou, Nyong et So’o Haut Nyong, Dja et Lobo, Mvila, Vallée du Ntem, Océan, Nyong et Kellé, Sanaga Maritime, Menchum, Dounga Mantung, Boyo, Mezam and Momo. However, Faro et Déo emerged only in 2018 as a subdivision with HIV infection hot-spots. Conclusion Despite the decrease in HIV epidemiology in Cameroon, this study has shown that there are spatial patterns for HIV infection in Cameroon and possible hot-spots have been identified. In its effort to eliminate HIV infection by 2030 in Cameroon, the public health policies may consider these detected HIV hot-spots, while maintaining effective control in other parts of the country.
In Cameroon, there is a national programme engaged in the control of schistosomiasis and soil-transmitted helminthiasis. In certain locations, the programme is transitioning from morbidity control towards local interruption of parasite transmission. The volcanic crater lake villages of Barombi Mbo and Barombi Kotto are well-known transmission foci and are excellent context-specific locations to assess appropriate disease control interventions. Most recently they have served as exemplars of expanded access to deworming medications and increased environmental surveillance. In this paper, we review infection dynamics through time, beginning with data from 1953, and comment on the short- and long-term success of disease control. We show how intensification of local control is needed to push towards elimination and that further environmental surveillance, with targeted snail control, is needed to consolidate gains in preventive chemotherapy as well as empower local communities to take ownership of interventions.
The current study was conducted in Garoua, Pitoa, and Mayo-Oulo health districts of north Cameroon, in order to investigate the resting behaviour of deltamethrin-resistant Anopheles (An.) gambiae s.l. populations and build a model of their response to the use of Permanet 2.0 long-lasting insecticidal nets (LLINs). Adult mosquitoes were collected in October and November 2011, using spray catches and window exit traps in 29 clusters with LLINs in use. Sampled An. gambiae s.l. were identified down to species and analysed for blood-meal origin, physiological and circumsporozoite protein status. Deltamethrin resistance was assessed using World Health Organization's (WHO's) standard protocol. A two-level ordinary logit model was used to relate the resting behaviour and deltamethrin resistance. Identified species of the An. gambiae complex included An. arabiensis (90.6%), An. coluzzii (7.1%) and An. gambiae s.s. (2.3%). They displayed 1.1-4.8% infection rates, 80% indoor-resting density and 56-80% human blood index. Eleven An. gambiae s.l. populations over the 15 tested were resistant to deltamethrin (51-89.5% mortality rates). Model results showed a significant dependence of indoor vector density
Today, Linear Mixed Models (LMMs) are fitted, mostly, by assuming that random effects and errors have Gaussian distributions, therefore using Maximum Likelihood (ML) or REML estimation. However, for many data sets, that double assumption is unlikely to hold, particularly for the random effects, a crucial component in which assessment of magnitude is key in such modeling. Alternative fitting methods not relying on that assumption (as ANOVA ones and Rao's MINQUE) apply, quite often, only to the very constrained class of variance components models. In this paper, a new computationally feasible estimation methodology is designed, first for the widely used class of 2-level (or longitudinal) LMMs with only assumption (beyond the usual basic ones) that residual errors are uncorrelated and homoscedastic, with no distributional assumption imposed on the random effects. A major asset of this new approach is that it yields nonnegative variance estimates and covariance matrices estimates which are symmetric and, at least, positive semi-definite. Furthermore, it is shown that when the LMM is, indeed, Gaussian, this new methodology differs from ML just through a slight variation in the denominator of the residual variance estimate. The new methodology actually generalizes to LMMs a well known nonparametric fitting procedure for standard Linear Models. Finally, the methodology is also extended to ANOVA LMMs, generalizing an old method by Henderson for ML estimation in such models under normality.
Restricted Maximum Likelihood (REML) is the most recommended approach for fitting a Linear Mixed Model (LMM) nowadays. Yet, as ML, REML suffers the drawback that it performs such a fitting by assuming normality for both the random effects and the residual errors, a dubious assumption for many real data sets. Now, there have been several attempts at trying to justify the use of the REML likelihood equations outside of the Gaussian world, with varying degrees of success. Recently, a new fitting methodology, code named 3S, was presented for LMMs with only added assumption (to the basic ones) that the residual errors are uncorrelated and homoscedastic. Specifically, the 3S-A1 variant was designed and then shown, for Gaussian LMMs, to differ only slightly from ML estimation. In this article, using the same 3S framework, we develop another iterative nonparametric estimation methodology, code named 3S-A1.RE, for the kind of LMMs just mentioned. However, we show that if the LMM is, indeed, Gaussian with i.i.d. residual errors, then the set of estimating equations defining any 3S-A1.RE iterative procedure is equivalent to the set of REML equations, but while including the nonnegativity constraints on all variance estimates, as well as positive semi-definiteness on all covariance matrices. In numerical tests on some simulated and real world clustered and longitudinal data sets, our new methods proved to be highly competitive when compared to the traditional REML in the R statistical software.
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