Public health round-up

Humphreys, Gary

doi:10.2471/blt.21.010821

Cited by 3 publications

(4 citation statements)

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“…Interestingly, third bi-week or power 2 (LST day[ 3 ]) and fourth bi-week or power 3 (LST day[ 4 ]) were positively associated with malaria cases with proportion 0.1 (BCI: 0.05–0.17) and proportion 0.22 (BCI: 0.22–0.2357) respectively. The increase in malaria cases changes in the fifth bi-week or power 4 (LST day[ 5 ]) where the proportion of cases begin to decrease.…”

Section: Resultsmentioning

confidence: 99%

“…The third bi-week or power 4 was positively associated with malaria which changed in bi-week of power 4 where there was a negative association. On the other hand NDVI had less effect on malaria for the first two lags until power 3 (NDVI [ 4 ]) which showed increase in malaria cases with proportion 0.06359 (BCI: 0.05515–0.06808). Similarly to the first DLM model, malaria cases decreased with increasing rainfall as shown in power 1 (Rain[ 2 ]) and power 3 (Rain [ 4 ] with proportion -0.78 (BCI: − 0.95– − 0.19) and proportion − 0.01 (BCI: − 0.017– − 0.005) respectively.…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand NDVI had less effect on malaria for the first two lags until power 3 (NDVI [ 4 ]) which showed increase in malaria cases with proportion 0.06359 (BCI: 0.05515–0.06808). Similarly to the first DLM model, malaria cases decreased with increasing rainfall as shown in power 1 (Rain[ 2 ]) and power 3 (Rain [ 4 ] with proportion -0.78 (BCI: − 0.95– − 0.19) and proportion − 0.01 (BCI: − 0.017– − 0.005) respectively. Interestingly rainfall was positively associated with malaria in power 2 (Rain [ 3 ]) with proportion 0.23 (BCI: 0.22–0.25).…”

Section: Resultsmentioning

confidence: 99%

“…To be eligible for WHO certification as malaria free, countries must achieve at least 3 consecutive years of zero indigenous cases. Since 2015, about 9 countries have been certified by the WHO Director-General as malaria-free and they include Maldives (2015), Sri Lanka (2016), Kyrgyzstan (2016), Paraguay (2018), Uzbekistan (2018), Argentina (2019), Algeria (2019), China (2021) and El Salvador (2021) [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Geostatistical Modeling to Assess Malaria Seasonality and Monthly Incidence Risk in Eswatini

Dlamini

Fall

Mabaso

2022

J Epidemiol Glob Health

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Eswatini is on the brink of malaria elimination and had however, had to shift its target year to eliminate malaria on several occasions since 2015 as the country struggled to achieve its zero malaria goal. We conducted a Bayesian geostatistical modeling study using malaria case data. A Bayesian distributed lags model (DLM) was implemented to assess the effects of seasonality on cases. A second Bayesian model based on polynomial distributed lags was implemented on the dataset to improve understanding of the lag effect of environmental factors on cases. Results showed that malaria increased during the dry season with proportion 0.051 compared to the rainy season with proportion 0.047 while rainfall of the preceding month (Lag2) had negative effect on malaria as it decreased by proportion − 0.25 (BCI: − 0.46, − 0.05). Night temperatures of the preceding first and second month were significantly associated with increased malaria in the following proportions: at Lag1 0.53 (BCI: 0.23, 0.84) and at Lag2 0.26 (BCI: 0.01, 0.51). Seasonality was an important predictor of malaria with proportion 0.72 (BCI: 0.40, 0.98). High malaria rates were identified for the months of July to October, moderate rates in the months of November to February and low rates in the months of March to June. The maps produced support-targeted malaria control interventions. The Bayesian geostatistical models could be extended for short-term and long-term forecasting of malaria supporting-targeted response both in space and time for effective elimination.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bayesian Geostatistical Modeling to Assess Malaria Seasonality and Monthly Incidence Risk in Eswatini

Dlamini

Fall

Mabaso

2022

J Epidemiol Glob Health

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Genetic Diversity of Bundibugyo Ebolavirus from Uganda and the Democratic Republic of Congo

Omara

Muyingo

Balinandi

et al. 2021

Preprint

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Background The Ebolavirus is one of the deadliest viral pathogens which was first discovered in the year 1976 during two consecutive outbreaks in the Democratic Republic of Congo and Sudan. Six known strains have been documented. The Bundibugyo Ebolavirus in particular first emerged in the year 2007 in Uganda. This outbreak was constituted with 116 human cases and 39 laboratory confirmed deaths. After 5 years, it re-emerged and caused an epidemic for the first time in the Democratic Republic of Congo in the year 2012 as reported by the WHO. Here , 36 human cases with 13 laboratory confirmed deaths were registered. Despite several research studies conducted in the past, there is still scarcity of knowledge available on the genetic diversity of Bundibugyo Ebolavirus . We undertook a research project to provide insights into the unique variants of Bundibugyo Ebolavirus that circulated in the two epidemics that occurred in Uganda and the Democratic Republic of Congo Materials and Methods The Bioinformatics approaches used were; Quality Control, Reference Mapping, Variant Calling, Annotation, Multiple Sequence Alignment and Phylogenetic analysis to identify genomic variants as well determine the genetic relatedness between the two epidemics. Overall, we used 41 viral sequences that were retrieved from the publicly available sequence database, which is the National Center for Biotechnology and Information Gen-bank database. Results Our analysis identified 14,362 unique genomic variants from the two epidemics. The Uganda isolates had 5,740 unique variants, 75 of which had high impacts on the genomes. These were 51 frameshift, 15 stop gained, 5 stop lost, 2 missense, 1 synonymous and 1 stop lost and splice region. Their effects mainly occurred within the L-gene region at reference positions 17705, 11952, 11930 and 11027. For the DRC genomes, 8,622 variant sites were identified. The variants had a modifier effect on the genome occurring at reference positions, 213, 266 and 439. Examples are C213T, A266G and C439T. Phylogenetic reconstruction identified two separate and unique clusters from the two epidemics. Conclusion Our analysis provided further insights into the genetic diversity of Bundibugyo Ebolavirus from the two epidemics. The Bundibugyo Ebolavirus strain was genetically diverse with multiple variants. Phylogenetic reconstruction identified two unique variants. This signified an independent spillover event from a natural reservoir, rather a continuation from the ancestral outbreak that initiated the resurgence in the year 2012. Therefore, the two epidemics were not genetically related.

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Chitosan Biguanidine/PVP Antibacterial Coatings for Perishable Fruits

et al. 2022

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World hunger is on the rise, yet one-third of food is wasted. It is necessary to develop an effective food preservation method to reduce food waste. This article reports a composite film based on chitosan biguanidine hydrochloride(CBg) and poly (N-vinyl-2-pyrrolidone)(PVP) that can be used as a conformal coating for fresh produce. Due to the strong positive charge of CBg, the film has excellent antibacterial properties. Owing to the hydrogen bonds between CBg and PVP, the film has good flexibility and mechanical properties. In addition, the coating is washable, transparent, and can reduce the evaporation of water. The above characteristics mean the film has broad application prospects in the field of food preservation.

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Public health round-up

Cited by 3 publications

References 0 publications

Bayesian Geostatistical Modeling to Assess Malaria Seasonality and Monthly Incidence Risk in Eswatini

Bayesian Geostatistical Modeling to Assess Malaria Seasonality and Monthly Incidence Risk in Eswatini

Genetic Diversity of Bundibugyo Ebolavirus from Uganda and the Democratic Republic of Congo

Chitosan Biguanidine/PVP Antibacterial Coatings for Perishable Fruits

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