-
Notifications
You must be signed in to change notification settings - Fork 0
/
veo vampiros muertos.R
185 lines (134 loc) · 5.38 KB
/
veo vampiros muertos.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
###################################################################################
# modelagem matematica lobisomem vampiro humanos #
###################################################################################
# Formulacao com fracoes (proporcoes)
# Carregando pacotes ----
library(deSolve)
library(tidyverse)
library(reshape2)
# Parametros ----
# Taxas por ano
natalidade <- 0.000001 #taxa matalidade
Motalidade.Hum <- 0.0001 #taxa de mortalidade
beta.vamp <- 0.001 #taxa de infecao vampiros
beta.lobi <- 0.001 #taxa de infecao lobisomn
prev.vamp <- 0.0001 # taxa de prevencao que um humano vire vamp porque se suicida ou le disparam
prev.lobi <- 0.0001 #taxa de prevencao que um humano vire lobi porque se suicida ou le disparam
gamma.vamp <- 0#365/21 # periodo de latencia para virar vampiro 3 sem
gamma.lobi <- 0 #365/21 # periodo de latencia lobi 3 sem
letha.homen.mata.vampiro <- 0 # taxa de humonaos que matam vampiros
letha.lobi.mata.vampiro <- 0 #taxa de lobi que matam vampiros
letha.homenm.mata.lobi <- 0 # taxa de humonaos que matam lobi
letha.vampi.mata.lobi <- 0 # taxa de vampiro que matam lobi
mortalidade.lobi <- 0 # taxa de morte natural dos lobi
# o desolve precisa um conjunto de parametrros pra souber o nome da equacao
par.SVW <- c(
natalidade = natalidade,
Motalidade.Hum = Motalidade.Hum,
beta.vamp = beta.vamp,
beta.lobi = beta.lobi,
prev.vamp = prev.vamp,
prev.lobi = prev.lobi,
gamma.vamp = gamma.vamp,
gamma.lobi = gamma.lobi,
# gamma.vamp = gamma.lobi,
# gamma.lobi = gamma.vamp,
letha.homen.mata.vampiro = letha.homen.mata.vampiro,
letha.lobi.mata.vampiro = letha.lobi.mata.vampiro,
letha.homenm.mata.lobi = letha.homenm.mata.lobi,
letha.vampi.mata.lobi = letha.vampi.mata.lobi,
mortalidade.lobi = mortalidade.lobi
)
# Calculando R0 ----
#
R0 <- beta.vamp/(gamma.vamp)
R0
# Variaveis e condicao inicial ----
S <- 1000
Iv <- 0
Iw <- 0
W <- 0
V <- 0
# state.SIR <- c(s=0.9999,i=0.0001,r=0)
state.SVW <- c(S = S, Iv = Iv,Iw = Iw, W = W, V = V)
# Tempo de simulacao ----
# Varie o tempo de simulacao: 20, 100
tsim <- 1000
Dt <- 1
# Funcao para o modelo SIR ----
# Termo de transmissao frequencia dependente
SIRS <- function(t,state,parameters){
with(as.list(c(state,parameters)),{
# # rate of change
# ds <- mu - beta*s*i - mu*s + + omegaR*r
# di <- beta*s*i - (mu+gama)*i
# dr <- gama*i - mu*r - omegaR*r
ds <- natalidade*S - Motalidade.Hum*S - beta.vamp *S*V -beta.lobi*S*W
dIv <- beta.vamp*S*V - prev.vamp* Iv + gamma.vamp*Iv
dV <- gamma.vamp*Iv - letha.homen.mata.vampiro*V - letha.lobi.mata.vampiro*V
dIw <- beta.lobi*S*W - prev.lobi*Iw + gamma.lobi*Iw
dW <- gamma.lobi*Iw - letha.homenm.mata.lobi*W - letha.vampi.mata.lobi*W - mortalidade.lobi*W
# return the output of the model
return(list(c(ds, dIv, dV, dIw, dW)))
})
}
tempos <- seq(from=0,to=tsim,by=Dt)
# modSIRS <- ode(y = state.SIR, times = tempos, func = SIRS, parms = par.SIRS, method = "ode45")
modSIRS <- ode(y = state.SVW, times = tempos, func = SIRS, parms = par.SVW, method = "ode45")
modSIRS <- as.data.frame(modSIRS)
modSIRS %>%
gather(key = 'compartimento', value = 'valor', -time)%>%
ggplot(aes(x= time, y = valor))+
geom_line()+
facet_wrap('compartimento', scales = 'fixed')
plot(modSIRS$time, modSIRS$Iv)
# names(modSIRS) <- c("t","s","i","r")
head(modSIRS)
# # Grafico de infectados ----
# plot(modSIR$t, modSIR$i, col="red", type="l")
#
# # Exportando o grafico para o formato tif
# # help(tiff) para mais informacoes
# tiff(filename="InfectadosModeloSIR.tif", width=100, height=80, pointsize=8,
# units="mm", res=300, compression="lzw")
#
# plot(modSIR$t, modSIR$i, col="red", type="l",
# xlab="Tempo (anos)", ylab="Proporção de infectados")
#
# dev.off()
#
# # Grafico de s, i, r ---
# plot(modSIR$t, modSIR$s, col="blue", type="l", ylim=c(0,1))
# lines(modSIR$t, modSIR$i,col="red")
# lines(modSIR$t, modSIR$r,col="green")
#
# Para verificar a soma das proporcoes
# modSIR$n <- modSIR$s + modSIR$i + modSIR$r
# Grafico no ggplot2 ----
# Carregando pacotes
library(ggplot2)
library(reshape)
# Empilhando os dados para o grafico ----
modSIRS <- melt(as.data.frame(modSIRS),id="t")
names(modSIRS) <- c("t", "compartimento", "proporcao")
# Grafico basico no ggplot2
grafSIR <- ggplot(modSIRS,aes(x=t,y=proporcao,colour=compartimento,group=compartimento)) +
geom_line(size=1)
grafSIR
# Modificando o grafico no ggplot2
grafSIRs2 <- ggplot(modSIRS,aes(x=t,y=proporcao,colour=compartimento,group=compartimento)) +
geom_line(size=1) +
theme(axis.text.x = element_text(size=14, colour="black"),
axis.text.y = element_text(size=14, colour="black"),
axis.title.x = element_text(colour="black", size=14, vjust=0.1, hjust=0.5, face="plain"),
axis.title.y = element_text(colour="black", size=14, vjust=0.2, hjust=0.5, face="plain"),
legend.position=c(0.85,0.3))+
xlab("Tempo (anos)") +
ylab("Proporção")
grafSIRs2
# Exportando o grafico para o formato tif
# help(tiff) para mais informacoes
tiff(filename="ModeloSIR_ggplot.tif", width=100*2, height=80*2, pointsize=8,
units="mm", res=300, compression="lzw")
grafSIR2
dev.off()