Table 1.

Maximum likelihood of fits for different drug periods and different transmission dependence

		Ndiop								Dielmo
		Quinine, $n=26$		Chloroquine, $n=95$		Fansidar, $n=30$		ACT, $n=21$		Quinine, $n=57$		Chloroquine, $n=106$		Fansidar, $n=31$		ACT, $n=27$
Model	p	$\ell$	${\text{AIC}}_c$	$\ell$	${\text{AIC}}_c$	$\ell$	${\text{AIC}}_c$	$\ell$	${\text{AIC}}_c$	$\ell$	${\text{AIC}}_c$	$\ell$	${\text{AIC}}_c$	$\ell$	${\text{AIC}}_c$	$\ell$	${\text{AIC}}_c$
EIR	4	−141	292	−433	874	−143	296	−94	198	−227	463	−398	804	−134	277	−121	252
Sp	3	−113	233	−381	768	−124	255	−90	187	−223	452	−399	804	−128	263	−122	251
SpTR	5	−109	231	−378	766	−121	254	−86	186	−223	457	−391	792	−121	254	−115	243
SpROT	6	−117	250	−521	1055	−119	254	−87	192	−222	458	−392	797	−121	257	−115	246

For each drug period transmission was modeled by means of the entomological inoculation rate (EIR), only with splines (Sp) and with splines and anomalies of rain and temperature (SpTR and SpROT). The p-labeled column corresponds to the number of free parameters. The rest of the parameters were fixed at the maximum-likelihood estimated values listed in Tables S3 and S4. The second-order Akaike information criterion (${\text{AIC}}_c$) is computed as ${\text{AIC}}_c=-2\ell +2p+\left(2p\left(p+1\right)\right)/\left(n-p-1\right)$ with n the number of observations. The best fits are shown in boldface type. Overall the fit improves when temperature and rainfall anomalies are considered (SpTR model).