Office excel

Analysis of quantitative data was carried out using Microsoft Office Excel basic functions and open-source software, RStudio version 1.3.1056 based on R version 4.0.2 [29 ,30 ]. Clustering of house type analysis was done using the klaR package clustering of categorical variables [31 ]. The effect of fire/smoke (indoor or outdoor) and large animals (indoor or outdoor) on mosquito landing rates, were separated into time periods (1800h – 0000h, and 0000h to 0600h), and were evaluated using Kruskal-Wallis test. With mosquito landing rates being normally distributed, the number of people in each house were evaluated using a regression analysis towards determining if they had an effect on mosquito numbers. Human behavior-adjusted biting rates and the protections afforded by ITNs were generated using HLRs, HBOs and ITN survey data as in Monroe et. al, 2020 [12 (link),13 (link)].

The data collected in this study were analysed and graphed by using Microsoft Office Excel (2010) and RStudio software (4.1.1), including the informant consensus factor (ICF) and use frequency (UF). A quantitative data analysis was conducted using the ICF method and the number of citations. The ICF was calculated as the ICF = (Nur − Nt)/(Nur − 1), where Nur is the sum of plant species used by all the respondents to treat a particular disease and Nt is the number of identical plant species used by all the respondents to treat a particular disease [24 ]. The use frequency of medicine sold on the market was estimated with the utilization frequency [25 (link)]. The UF is calculated as UF = Nm/Ni, where Nm is the number of use reports of medicinal materials mentioned by informants, and Ni is the total number of informants. High UF values indicate that the herb is used more frequently in the region.