ExpBites (version 0.1.3)

calculate_Exp: Calculate Hourly Mosquito Biting Exposure for Bednet Users and Non-Users

Description

Using a model of behavioral interactions, computes the hourly exposure to mosquito bites for both bednet users and non-users, based on their location (indoors, outdoors, under a net) and mosquito biting rates. For users, exposure is adjusted for the protective effect of bednets.

Usage

calculate_Exp(df, df_bites, protection = 0.92)

Value

A dataframe with one row per hour (0–23) and the following columns:

• Bi_t – indoor biting rate,

• Bo_t – outdoor biting rate,

• N – number of human individuals,

• Np – number of human individuals that are net users,

• It – proportion of people indoors (users and non-users),

• Eui – mean exposure of non-users indoors,

• Euo – mean exposure of non-users outdoors,

• Eu – total mean exposure of non-users,

• p_in – proportion of people (users) being indoors,

• p_net – proportion of people (users) being asleep (under net),

• Epi – mean exposure of users indoors (not under net),

• Epn – mean exposure of users indoors under bed net (adjusted by protection),

• Epo – mean exposure of users outdoors,

• Epp – mean exposure prevented by bed net use,

• Ep – total mean exposure of users.

Arguments

df

A dataframe with individual-level data, including columns:

• individual – individual ID,

• hour – hour of the day (0–23),

• state – activity/location state (1 = outdoors, 2 = indoors awake, 3 = indoors asleep, 4 = indoors asleep under bed net),

• bednet_user – logical indicator of bed net use (TRUE/FALSE).

df_bites

A dataframe of biting rates by hour, with columns:

• hour – hour of the day (0–23),

• indoor_bites – number of bites indoors,

• outdoor_bites – number of bites outdoors.

protection

A numeric value (default = 0.92, according to corbelFieldEfficacyNew2010;textualExpBites for Permanet 2 ITNs) indicating the proportion of bites prevented by bednet use.

Details

The mathematical model of behavioral interactions is an extension of the killeenQuantifyingBehaviouralInteractions2006;textualExpBites model as previously described in geissbuhlerInterdependenceDomesticMalaria2007;textualExpBites and moirouxHumanExposureEarly2014;textualExpBites

References

Examples

# generate fake data 
df <- gen_df_human(n_individuals = 100, hours = c(0:9,17:23))
df_bites <- gen_df_mosquito() 
# calculate mean hourly exposure to bites
exposure_results <- calculate_Exp(df, df_bites)